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Prof. Shweta Agrawal, CSE | "Real-life cryptography is cooler than Imitation Game-the movie"| Ep.13

Ever wondered why even supercomputers can't crack certain codes? Or why the most secure algorithms are inspired by art? This episode unravels these mysteries as Professor Agrawal takes us through: - The hidden beauty of mathematical secrets - Why modern cryptography is cooler than Hollywood depicts - Her journey of merging art with theoretical computer science - Building CyStar and redefining cybersecurity in India Discover why calling computer science "coding" is as absurd as calling surgery "knife science," and explore the delicate dance between structure and randomness that keeps our digital world secure. A profound discussion about quantum-resistant algorithms, the power of Atma Shraddha, and much more. Plus, discover how medical research can use private genomic data without compromising privacy, why RSA encryption was a life-changing moment, and how the war effort transformed modern cryptography. An unmissable episode for anyone interested in mathematics, computer science, cybersecurity, or the future of digital privacy. Chapters: 00:00:00 Introduction 00:01:35 What is Cryptography? 00:02:24 History of Cryptography 00:04:20 Caesar Cipher and Hard Problems 00:05:09 Real-World Applications of Cryptography 00:07:07 Eavesdroppers and Attackers 00:08:01 Defining "Hard" Problems 00:09:24 Algorithms in Cryptography 00:10:11 Public Key Encryption- RSA 00:12:44 P vs NP 00:17:18 Shweta's Research Interests 00:17:57 Computing on Encrypted Data 00:18:53 Examples of Computing on Encrypted Data 00:19:55 The Fine Line Between Structure and Randomness 00:20:30 Analogy for Encrypted Computation 00:24:00 Functional Encryption 00:25:13 Functional Encryption and Quantum Security 00:25:41 Attribute-Based Cryptography 00:26:49 Lattice-Based Cryptography 00:28:10 Hard Problems in Engineering vs. Computer Science 00:29:59 Subfields of Computer Science 00:34:31 Why Study Computer Science? 00:37:56 Pressures on Computer Science Students 00:39:42 India's Position in Cryptography 00:41:38 CyStar Center 00:42:28 CyStar as a Cybersecurity Focused Center 00:46:40 Women in STEM 00:49:02 Performance of Women in STEM 00:51:35 Shweta's Personal Journey 00:55:38 Decision to Return to India 00:57:43 Atma Shraddha (Self-Belief) 01:03:23 Art and Cryptography 01:05:00 Conclusion References Prof. Shweta: https://www.cse.iitm.ac.in/~shwetaag/ CyStar: https://cystar.iitm.ac.in/ To know more about what makes IIT Madras- the Best Place to Build- go on to https://www.bestplacetobuild.com/

Shweta AgrawalguestUnknown Hosthost
Feb 14, 20251h 5mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:001:35

    Introduction

    1. SA

      Calling our field computer science is like calling surgery knife science. Cryptography, in a sentence, is the art of keeping secrets. The desire to keep secrets is as old as humanity itself. Cryptography is really this very fine dance between sort of structure and randomness. I remember reading this algorithm as an undergrad and feeling that this is a life-changing moment. [upbeat music]

    2. UH

      Hi, my name is Amrit. We've heard that IIT Madras is the best place to build. [upbeat music] So we've come down to the Sudha and Shankar Innovation Hub. We want to meet some people. These are builders. We want to talk to them about their work, and also ask them, what makes IIT Madras the best place to build? [upbeat music] Hi, welcome to the Best Place to Build Podcast. Today, we are sitting with Professor Shweta Agrawal. She's a professor of computer science at IIT Madras. Her area of interest is cryptography, so I've brought my ruled notebook to take notes. Uh, she holds the Venky Hariharan and Anand Rajaram Chair, and has won numerous awards, including the Swarnajayanti in 2020. Welcome, ma'am.

    3. SA

      Thank you so much. It's good to be here.

    4. UH

      Ma'am, let's start with,

  2. 1:352:24

    What is Cryptography?

    1. UH

      uh, what is cryptography? I think, uh, maybe it's obvious in your world, but it's completely alien to me.

    2. SA

      So cryptography, in a sentence, is the art of keeping secrets. Uh, essentially, there are different goals that you might have, uh, such as, you know, wanting to communicate with someone in private, or when you're talking to someone, you, you may want to convince them that, you know, you are the person that they think, uh, they are talking to. So there are different goals that, uh, you might want to achieve in a secret manner, right? And cryptography is the science of enabling that.

    3. UH

      So what I know of this, from whatever you said, is, uh... So I've watched Imitation Game, which is a movie about, uh-

    4. SA

      Yeah

    5. UH

      ... about Alan Turing-

    6. SA

      Right

    7. UH

      ... his team breaking the Enigma code. So c- can you tell us where we started from in cryptography and where we have

  3. 2:244:20

    History of Cryptography

    1. UH

      come to?

    2. SA

      Yeah, so cryptography, uh, is really an ancient field, though it may not have been, you know, uh, given, given this name until much more recently. But it's, uh... You know, the, the desire to keep secrets is as, uh, old as humanity itself, right? So y- you have, like, these very, very old codes, like the Caesar cipher and so on, where essentially if I want to say something in private to you, I replace each letter by, you know, the letter five steps ahead of it or something like that. So, uh, the field in, in its original form, I think has always existed, as long as humanity has existed. Uh, the movie you mentioned, and I mean, uh, the, the war and the efforts of building, uh, unbreakable codes, of course, took on, uh, like, a profound importance, uh, during that time. And, uh, the field as we know it is actually also, in a way, inspired from what happened, uh, during the war, which is that, uh, you know, a, a bunch of really smart people put together a really, uh, hard code to break, what they thought was a hard code to break, but they missed something, right? And that's what led to the, the break of the code. So modern cryptography seeks to kind of address this problem by saying that, "You know, we will prove to you mathematically that the only way that you can break our code is by solving some, you know, very hard mathematical problem." So in some sense, you know, all attack vectors can be reduced down to, like, this one question of: Can you solve so and so problem? And then if you choose this underlying problem as something that, you know, maybe mathematicians have tried for, like, decades, uh, and you have a lot of confidence that this problem is going to be very hard for any attacker, then you can have much more confidence that your code is secure.

    3. UH

      Okay.

    4. SA

      So that is the field of modern cryptography.

    5. UH

      I understood. So let me just say that, for example,

  4. 4:205:09

    Caesar Cipher and Hard Problems

    1. UH

      if I use a Caesar cipher, then the code to break is that I have to figure out that this is five letters ahead, and I just have to go back five letters. But we have come to a place where figuring out how to break this code has become much harder.

    2. SA

      Yeah.

    3. UH

      And I'll, I'll ask you in a bit about hard... What do you mean by hard? Because I think what I mean by hard is in sense of the word hard, but maybe you are using a more mathematical, uh, definition. But before that, in the movie, and, and because we started from there, and because you said that a lot of it started from the war effort, in the movie, um, the Germans deve- developed this code, uh, and the, uh, Allied powers are trying to break it. It- is it that... Uh, I mean, it feels like a national security kind of issue, but why do we, as normal people or normal businesses, why should we

  5. 5:097:07

    Real-World Applications of Cryptography

    1. UH

      care about cryptography?

    2. SA

      So first of all, I mean, if you lose wars, uh, as citizens- [chuckles]

    3. UH

      Everybody has to. [chuckles]

    4. SA

      ... everybody has to care. But, uh, that- that's a good question you raised, because obviously, uh, it has a lot of real-world implications, even outside of defense considerations. So for instance, you know, all of us are doing online shopping now. Now, when you do online shopping, you use your credit card information, right? And this credit card, uh, number and the CVV code, et cetera, and the OTP, this is all traveling over the network. So if there's an eavesdropper who's listening in to this information, right, uh, she can intercept it and use it to make other purchases of, of her own. Well, not the OTP, but for instance, if, if it's just the card number and CVV.... so, uh, you need to encode this information for it to be secure, even to travel over the network, right? So this is a very basic example of where cryptography is used in day-to-day life, but there are lots of others. Like, let's say that, you know, two companies want to try, uh, want to figure out whether they, they should merge or not. Will it be profitable? Now, before the merger, you don't want to reveal your assets to the other company, but, uh, computing whether the merger is going to be profitable requires information about the assets. So one way that cryptography can enable this is that, you know, uh, you can run a, a so-called multiparty computation protocol-

    5. UH

      Okay

    6. SA

      ... where, uh, the two of us have private inputs, right? And we're interested in computing some common function, and there's a mathematical guarantee that, uh, each party will only learn the output of the function, but nothing else about the other person's input. So it has really very wide applications.

    7. UH

      Fair enough. I guess, um, if I'm... I mean, just reducing it back to the personal, if I want to share with a bank that I'm worthy of certain financial-

    8. SA

      Yeah

    9. UH

      ... investment, but don't want to divulge my assets?

    10. SA

      That's right.

    11. UH

      Okay. Great. So

  6. 7:078:01

    Eavesdroppers and Attackers

    1. UH

      you used the word eavesdropper, and then I think earlier you had used the word attacker.

    2. SA

      Yeah.

    3. UH

      Are these terms that are used often in this field?

    4. SA

      These are terms that, uh... yeah, they, they, uh, they get used in almost every sentence. But, uh, when, when we're thinking of these terms from the cryptographic perspective, really, these are not people, but algorithms. So, you know, we're, we're really thinking of an algorithm which takes some information and, you know, can it do something useful with that information? So-

    5. UH

      Okay. Okay.

    6. SA

      Yeah.

    7. UH

      So are these the same, by the way, this eavesdropper and attacker?

    8. SA

      No, they are not the same. So an eavesdropper is a particular type of attacker who, who passively listens on a channel, but an attacker could be able to do more. So, for example, an attacker, uh, could be an algorithm that not only listens, but also somehow changes what is being sent on the channel, right? So all kinds of attacks are possible.

    9. UH

      Fabulous. So let's go back

  7. 8:019:24

    Defining "Hard" Problems

    1. UH

      to the word hard. Uh, you used the word, um, hard problem.

    2. SA

      Yeah.

    3. UH

      I assume that there's some definition to that.

    4. SA

      Yeah. So, uh, you know, at a high level, by hard, what we mean in computer science is that no efficient probabilistic algorithm should be able to solve this problem. And by efficient, you know, uh, polynomial time is, is the technical term that we use. But, uh, for all practical purposes, this just means that, you know, your, your fastest supercomputer should not be able to solve this problem.

    5. UH

      Okay.

    6. SA

      All of them put together also should not be able to solve this problem in any reasonable time. And the reasonable time here, depending on your application, it could be 10 years, like, if it is just, you know, some very local application, or it could be, you know, the age of humanity. So you, you can kind of vary the kind of security that you ask from the scheme, uh, and, and set the hardness parameter accordingly.

    7. UH

      It's very interesting. Again, going back to the movie, because that's really [chuckles] my only reference point to cryptography, uh, what happens is that-

    8. SA

      I just want to say that real-life cryptography is cooler than this movie.

    9. UH

      [chuckles] Oh, fair enough. But to my point, there was a day limit, that the code changes every day.

    10. SA

      Yeah.

    11. UH

      So they had to s- they had to sort of race to solve it-

    12. SA

      Yeah

    13. UH

      ... within a day. I want to just dig deeper a little bit onto this, because maybe I don't understand it as well. I have a very superficial understanding of the word algorithm

  8. 9:2410:11

    Algorithms in Cryptography

    1. UH

      itself. Uh, so what does an algorithm mean in a theoretical sense?

    2. SA

      An algorithm is, uh, you know, just a sequence of steps that will solve some problem for you. So there, there is some problem, right? There is some input, and there is a specification of what output you want.

    3. UH

      Mm.

    4. SA

      And, uh, the algorithm is the sequence of steps that tells you how to go from the input to the output.

    5. UH

      Okay, and what would be an example of an algorithm that's used in encryption or decryption?

    6. SA

      You know, multiply a matrix with a vector.

    7. UH

      Okay.

    8. SA

      Uh, so the algorithm would be sort of, you know, uh, compute the inner product of the, the first row of the matrix with the vector, and then go to the next one. Maybe this is, uh, too superficial, uh, an example.

    9. UH

      I've heard

  9. 10:1112:44

    Public Key Encryption- RSA

    1. UH

      this word RSA, AES. What are these terms?

    2. SA

      Yeah, so I was just coming to that. So those are kind of more interesting examples of algorithms that are used in cryptography. The matrix vector is, is like a toy example. Uh, RSA is a very, very famous cryptosystem. Uh, it, it won its inventors the Turing Award, and it's actually the reason- the place where I fell in love with cryptography. Like, I, I remember reading this algorithm as an undergrad and, you know, feeling that this is a life-changing moment. So it's this very clever algorithm which, uh, shows you how to take, uh, some message and, you know, convert it into a ciphertext, what we call a ciphertext. So the input is the message, the output is the ciphertext. There's also some random coins that the algorithm flips along the way, and it does a sequence of operations that will convert the message into something that is unbreakable. So now the ciphertext, if I send it on the channel, you can, you know, uh, read it, you can try everything you want, but unless you can solve some very hard problem which is considered infeasible, you cannot reverse map it back to the message that you started with.

    3. UH

      Okay. Okay, but, but somebody will be able to reverse, because it has to be decrypted, right?

    4. SA

      Very good. So that's very important. So, uh, the point is that, you know, uh, RSA is a, what, what we call a public key encryption scheme. So here there, there is a public key and a secret key, and, uh, in order to encrypt, you only use the public key, so anybody can encrypt. So this encryption algorithm, anybody can run it, but the decryption algorithm needs a secret, uh, input which, which is the secret key. So now this kind of reduces your task to just keeping the secret key secure. So if the secret key is secure, then, uh, you know, your ciphertext is, uh-... uh, unbreakable, but of course, if you have the secret key, then you can get back the message.

    5. UH

      As you're saying this, I remember, uh, uh, some memes, internet memes around people sharing their private key or public key on GitHub or something like that. So that, that's- so I would generate a private key and a public key. I would share the public key with the, the people-

    6. SA

      Users

    7. UH

      ... I'm sharing with.

    8. SA

      Yeah.

    9. UH

      And give my private key to myself. That's the-

    10. SA

      Yeah

    11. UH

      ... idea. And so then I don't need to worry about what the RSA algorithm actually does, I just have to keep the private key.

    12. SA

      That's right. Yeah.

    13. UH

      Mm. Okay. Uh, so, um, uh, so far we have spoken about what is cryptography, P versus NP. Polynomial time is what you said, and, uh, what is a hard problem, and some algorithms we use.

    14. SA

      So we didn't

  10. 12:4417:18

    P vs NP

    1. SA

      actually talk about P versus NP, but this is, this is, uh, at the root of the, the hardness, uh, question. So P versus NP is the most famous problem of computer science, and, you know, uh, it, it's still open. So it, it's something that shows us, uh, how little we as humanity know, uh, about science, right? So it's a very, very basic question. P is, is the class of pro- problems that can be solved efficiently. NP is the class of problems whose solution can be verified efficiently. Now, obviously, if you can solve a problem efficiently, you can also check the answer efficiently just by solving it, right? But, uh, there, there can be problems which you don't know how to solve, but if you're given the answer, you can check if it's the right answer. And the question is: Are these two classes the same? So cryptography, at the very least, uh, requires that these two classes be different, because you, you should be able to base the hardness of, of your, uh, uh, s- scheme, like the security of your scheme, on some problem that cannot be solved efficiently.

    2. UH

      So if P is a set of problems and NP is another set of problems, there should be a problem in NP which is not in-

    3. SA

      Exactly. Exactly.

    4. UH

      And do, do problems move from here to there over time?

    5. SA

      Um, yeah, that's a good question. So yes, I mean, um, uh, there, there are problems for which, you know, we find polynomial time algorithms which were not known before. So certainly, yeah.

    6. UH

      Oh, so the P is polynomial time?

    7. SA

      Yeah, P-

    8. UH

      And why is it called polynomial times?

    9. SA

      Because, uh, technically speaking, the- when I say efficient, what we mean is that, uh, the running time of the algorithm is polynomial in, in the size of the problem. So if the problem took n bits to describe, right, then the time to solve the problem should be n to the c for some constant c. That's polynomial time.

    10. UH

      As opposed to exponential, or e to the...

    11. SA

      Exponential is, is, uh, yeah, beyond polynomial but, you know, there are also functions in the middle. But, yeah, you could think of exponential as being hard. Like, if, if you think that a problem will take exponential time to solve, this is a hard problem.

    12. UH

      Does the invention of quantum computers move more problems from NP to P? Is that-

    13. SA

      So actually, P and NP, the way that we define them are with respect to classical computers. I mean, you can define these problem, uh, these classes, uh, correspondingly in the quantum setting. And, uh, I think, uh, the short answer to what you're really asking is that, yes, there are problems that were classically, uh, considered hard that became easy in the quantum setting. And, uh, RSA, which we talked about earlier, is, is the most famous such example. So, so breaking an RSA, uh, cryptosystem is something that we believe is hard for classical computers. I say believe because remember, uh, I said that whether P and NP are the same is itself something that is unknown. Like it, it's conjectured, but, you know, we don't have a proof. So you can only conjecture that some problem is hard. And RSA is, uh, believed to be hard based on, you know, decades of research, uh, for classical computers. But for quantum computers, it- it's actually easy to solve.

    14. UH

      Mm. So a physical realization of quantum computers would sort of change the field entirely?

    15. SA

      I mean, the field has already changed entirely because for us, you know, the physical realization doesn't matter. Like, the algorithm is the physical realization. I mean, for, for-

    16. UH

      For theoretical computer scientists.

    17. SA

      For the- yeah, for theoretical computer scientists, uh, the algorithm is somehow more real than the computer, right? Because the, the computer may or may not be there, but the algorithm is already there. So you, you already care about, uh, using algorithms that, uh, don't have efficient quantum alga- uh, quantum solvers.

    18. UH

      Okay. So the, the description of a quantum computer already is there, so you already know that-

    19. SA

      Yeah

    20. UH

      ... uh, it can-

    21. SA

      Exactly

    22. UH

      ... it can solve RSA in polynomial time.

    23. SA

      Yeah.

    24. UH

      The physical realization of the quantum computer, I-

    25. SA

      May come now, may come later, but the point is, you know, it's something that we have to worry about now.

    26. UH

      Right. So if I could buy a quantum computer off Amazon, [chuckles] then I could theoretically break... I mean, practically break RSA algorithm.

    27. SA

      Yeah, I mean, this is extremely far away-

    28. UH

      Yeah

    29. SA

      ... uh, if at all, but, uh, sure. Yes. The answer is yes.

    30. UH

      Okay, understood. Um, with this background, can you

  11. 17:1817:57

    Shweta's Research Interests

    1. UH

      give us a little bit of idea on your own research interests and your-

    2. SA

      Yeah

    3. UH

      ... work?

    4. SA

      So I work in, uh, theoretical cryptography, and this, as, uh, I've been describing, is, is, you know, the science of designing algorithms that are secure to do interesting tasks, right? And, uh, the most basic task in cryptography is of encryption, and it's actually deep enough that, you know, you can sort of, uh, spend all your time just researching that. In, in a way, I have done that. Because encryption is not just about, uh, keeping data private. There are all kinds of other very interesting questions that you can ask about encryption. So, for example, if

  12. 17:5718:53

    Computing on Encrypted Data

    1. SA

      I want to, uh, encrypt my data, but I still want to do some meaningful computation on the data. Like, so for example, machine learning is a big thing now, where, you know, you have data and you want to run some algorithm and learn some- something at the end of it.... but in order to run this algorithm, everybody needs to share their data, right? But you may not trust the, the person who's collecting the data and, um, running this algorithm. So the question here that can be asked is, can you encrypt the data but still run your machine learning algorithm on it? And this is something which if you think about it for, for a bit, it seems like it should not be possible, right?

    2. UH

      Can I just, uh, the, the example that I have learned from your earlier talks is one of medical researcher-

    3. SA

      Yes, yes

    4. UH

      ... looking at pe- a mass number of people's-

    5. SA

      That's right

    6. UH

      ... health data.

    7. SA

      For example, or genomic data.

    8. UH

      Genomic data.

  13. 18:5319:55

    Examples of Computing on Encrypted Data

    1. SA

      So I often use this example because I think it's very relatable. Like, everybody will agree that, you know, medical research is one of the most important, uh, fields of research, right? And, uh, now you have a, uh, all this, uh, genomic data that could potentially be used for medical research. Like, maybe you can study, you know, how genomic patterns correlate with the, uh, efficacy of certain medication.

    2. UH

      Sure. For example, I want to solve for diabetes-

    3. SA

      Yeah

    4. UH

      ... and I want to, like, ask for the genomic data of-

    5. SA

      That's exactly

    6. UH

      ... 1 million diabetic patients.

    7. SA

      Yeah. Exactly. So now the point is that, uh, these 1 million diabetic patients are, are, uh, not going to be excited about sharing their genomic data with anybody because, you know, genomic data is something very, very private and sensitive.

    8. UH

      Yeah.

    9. SA

      So the question will be that can I encrypt it and still have you do the research you want?

    10. UH

      Sure.

    11. SA

      And now this is, uh, something which is really like walking this fine line, uh, of, you know, possible and impossible sort of structure and

  14. 19:5520:30

    The Fine Line Between Structure and Randomness

    1. SA

      randomness. Because, uh, if you think about it, when I say that a cipher is unbreakable, what does this really mean? It means that, you know, whatever I saw... Let's say I'm an attacker, whatever I saw, this should look completely-

    2. UH

      Mm

    3. SA

      ... random. It should look, you know, just like noise in some sense, right? Junk. It should look like junk. But now, um, what I'm saying is, i- if I want to compute, like if I want to add two numbers, okay, and I've encrypted them, I can add two numbers when they're given to me in the clear,

  15. 20:3024:00

    Analogy for Encrypted Computation

    1. SA

      but once I encrypted them, it's like I put them into two boxes that look exactly the same. They have to look exactly the same because if they didn't, I could distinguish which number is in which, right? Think of bits just zero and one.

    2. UH

      Yeah.

    3. SA

      So if both of them are put inside an, uh, encryption scheme, right, there, there's a ciphertext. This looks like a black box. Okay. Now, the, the shape and sort of... Think, think of a box. Like, the, the shape, size, and appearance of this box should not tell you anything about whether it's zero inside or one inside.

    4. UH

      Yeah.

    5. SA

      So in some sense, you know, encryption is, is making everything look the same.

    6. UH

      Sure. Mm.

    7. SA

      But now when I want to compute, let's say I want to add or multiply these numbers, right? I cannot, uh, do that if, if it's the same object.

    8. UH

      Sure, sure.

    9. SA

      Right?

    10. UH

      Yeah.

    11. SA

      So in some sense, you know, cryptography is really this very fine dance between sort of structure and randomness, where the randomness is, you know, that everything looks random. So there needs to be a computational hardness, which says I cannot recover zero and one given this box.

    12. UH

      Yeah.

    13. SA

      But there needs to be somehow some hidden structure in this, uh, random-looking object that allows me nevertheless to add, multiply, and eventually decrypt.

    14. UH

      So this image I'm getting in my mind is, uh, that, um... I don't know if this is accurate, but I will share it anyway. Suppose, uh, you know, you've seen these large container ships which have, like, 1,000 containers, uh, and so they all look the same. Maybe the color is slightly different. One is orange, one is blue or something. So there's some data inside the container. I need to run a computation on what is inside, uh, but I should not be able to see it, and, uh, I can only see containers.

    15. SA

      Yeah.

    16. UH

      But my algorithm can figure out whatever computation it needs without actually having access to the-

    17. SA

      Exactly. Exactly. So it's like saying... So here I'm not even allowing different colors, right? They- all these containers look the same. Let's say, you know, they're, they're just all boxes of the same shape and size and the same color. Now what I'm saying is I take, you know, 5 million black boxes, okay? Each one has the genomic data of 5 million people. I do some magic, right, and I, I give you a, a box which contains the answer of whatever you wanted to compute.

    18. UH

      Sure.

    19. SA

      And I manage to also give you a secret key, which opens this box in a way that the secret key can only open the box if the computation was done correctly. Like, if you did some illegal computation, which is to say, you know, you took 5 million boxes, but you said, "You know, the computation I'll actually do is just extract-

    20. UH

      Sure, gene sequence or a part

    21. SA

      ... gene sequence of, of so and so person."

    22. UH

      Yeah.

    23. SA

      Right? If this is the computation which you, you, you actually wanted to do, um, the key will not open that box.

    24. UH

      Okay.

    25. SA

      So only if you did the correct computation, which is, you know, whatever maybe everybody agreed. So everybody agreed that, okay, uh, you know, since I'm diabetic, I, I contribute my data to studying, uh, you know, some- something that will help diabetes, right? Everybody signs off on that, and now I, I can give a key which only opens the computation that everybody signed off.

    26. UH

      Understood. So the encryption is specific to that, uh, request, that, that-

  16. 24:0025:13

    Functional Encryption

    1. SA

      No, the encryption is not specific. The encryption is actually... It can be done. So that, that, that's a very good, uh, point because if the encryption were specific to that request, it would be a much easier problem. The encryption is just encryption. It, it, it's not married to any particular functionality, but I could, uh, you know, use, uh, different authorized functionalities-... to compute on the same ciphertext. Like, because a person is so many things, right? Like, so, uh, you know, maybe a person is diabetic, but you know, maybe she's also, I don't know, like, a ballet dancer or a professor or whatever. So you could be having a different study, let us say, that, you know, requires you to collect, uh, the data of all dancers together, and maybe she's also okay with that. So for each new study, she does not need to encrypt her data differently.

    2. UH

      Okay. I, I think- I feel like I understood [chuckles] that. Uh, is this what is called functional encryption?

    3. SA

      This is called functional encryption, yes.

    4. UH

      Okay.

    5. SA

      And this, this has been one of the core things that I have worked on, like designing algorithms to enable this.

    6. UH

      Okay. And-

    7. SA

      And in particular, since we talked about, uh, quantum, right? Uh, in particular,

  17. 25:1325:41

    Functional Encryption and Quantum Security

    1. SA

      the solutions I care about are, uh, those that, uh, we think will be secure even in the quantum regime. So even if there are quantum algorithms that are trying to break open the ciphertext, even then these constructions should remain secure.

    2. UH

      I've also... I, I, I went through the, the page that, uh, the department maintains, um, on, on your work, and I noticed something called, uh, attribute-based

  18. 25:4126:49

    Attribute-Based Cryptography

    1. UH

      cryptography.

    2. SA

      Yeah.

    3. UH

      Is this related?

    4. SA

      This is related. This can be seen as a special case of functional cryptography. So here you're not actually computing on, uh, the private information, you're computing on sort of attributes of that information. So, uh, you know, think of a file, uh, whose contents are secret, but, uh, I encrypt it against some kind of access control policy. So the access control policy could say that, you know, only, uh, members of the computer science department at IIT Madras, you know, who, who are, uh, uh, staff or faculty, but not students, should be able to open this file. Something like that. Then, uh, I can give out keys to people, right, based on their attributes. So if you're a professor, you have, uh, the attribute professor. If you, if you're a student, you have the attribute student in your key, and it will kind of come together and work that, you know, only if your attributes satisfy the access control policy can the file be decrypted.

    5. UH

      Nice. Interesting. So, okay, I have a sense of this. Um, I also read something called lattice-based

  19. 26:4928:10

    Lattice-Based Cryptography

    1. UH

      cryptography.

    2. SA

      Yeah.

    3. UH

      Is this also a field of-

    4. SA

      No. So this is, this is not, uh, uh, an application. Lattice-based cryptography basically means, you know, building cryptography from hard problems on lattices. So these- this is a class of hardness assumptions. This is a class of hard problems. So sort of these are problems that would replace, for instance, factoring or discrete logarithm or other, uh, problems that were quantum broken. And the cool thing about lattices is that, uh, maybe they receive a lot of attention now because, uh, uh, they are conjectured to be post-quantum secure. But, uh, the cool thing is that the mathematical structure that they offer is so rich and so different from the previous number theoretic, uh, structures that we had, that you can do all kinds of, you know, very cool new cryptography that was not possible, uh, before. So it's, it's not just a solution to... or, you know, a conjectured solution to the quantum threat, but it's, it's also a door of opportunity. Like, it, it can lead you to cryptography that- that's really new and different and much more powerful than what we knew before.

    5. UH

      Okay, cool. Um, I, I also want to, sort of from my reference point of how I understand, uh, the, uh, the word hard problem. Uh,

  20. 28:1029:59

    Hard Problems in Engineering vs. Computer Science

    1. UH

      in, in my engineering course, we, we learned the Navier-Stokes equation, and we heard that that's a hard problem. Is, is it the same thing?

    2. SA

      So, I, I don't know about this particular hard problem, but essentially when we say that the problem is hard to solve, it just means that, you know, if I'm given the input... So let's take factoring, because I think everybody knows this problem.

    3. UH

      Sure.

    4. SA

      So the factoring problem is that, you know, I give you a number which is the product of two large prime numbers, and I want to figure out the factor of that- of the factors of this number, right? So now a trivial way to solve this problem is kind of just go-

    5. UH

      Brute force

    6. SA

      ... brute force, right? You know, try two, three, five, seven. You know, just keep trying all the prime numbers till you hit a factor. Now, this process, right, it takes exponential time in the size of the input. So if the number is n, right, it can be represented in log n bits, and this brute force algorithm will take time, uh, root n, which is exponential in log n, right? So, uh, basically, the, the conjecture there is that more or less, this is the best you can do. So I say more or less, because there are more clever things that you can do than just this-

    7. UH

      Brute force

    8. SA

      ... brute force, but nothing that kind of changes the order of, of hardness significantly-

    9. UH

      Sure

    10. SA

      ... for a classical algorithm.

    11. UH

      Okay, understood. I will go back to my notes and check, uh, whether Navier-Stokes qualifies.

    12. SA

      [chuckles]

    13. UH

      Mm. Can I just zoom back a bit? And you are a professor in the computer science department.

    14. SA

      Yeah.

    15. UH

      Um, the way, um, uh, at an undergrad level or at a postgrad level, people understand the word computer science engineering is quite different from how a normal person understands

  21. 29:5934:31

    Subfields of Computer Science

    1. UH

      it. Because a normal person would think, "Oh, he's a computer science student, he does coding, uh, probably codes for some company." But that's not how, um, a computer science faculty would look at it, right? So I think you were explaining to me before this call that broadly, computer science has certain subfields. Can you just say that?

    2. SA

      Yeah. So sure. So actually, uh, it's interesting what you said because, uh, yesterday I, I taught my first class in Theory of Computation, which is the course I'm teaching this semester, and I spent a good part of the lecture exactly debunking this myth. That, you know, uh, computer science is not-... about coding, uh, you know, not about programming. So there's a very famous quote by, uh, a computer scientist called, uh, Dijkstra, and he says that, "Calling our field computer science is like calling surgery knife science." So a computer is, is just a physical realization of, you know, some of the ideas in the field. But our field is not about computers, it's about computing. You know, what can be computed? What is a hard problem? What is an easy problem? Are there some problems that are as hard as each other, right? Can I have efficient algorithms to solve these problems? When I say efficient, you know, how much time does it take? How much space does it take? How much parallel time does it take? If these problems, uh, cannot be solved, can I build mathematical codes? So not program- uh, programming codes, but-

    3. UH

      Algorithms.

    4. SA

      Algorithms. Can I build cryptography from it, right? So these are the kinds of questions that, uh, are asked in, in the science of computing. Now, the science of computing has developed a lot, right? So, uh, there are also a, a lot of really cool things that have happened since we started asking these questions, like we built computers, which is obviously very cool. We can now have fields that study, you know, how to build an operating system. You know, how to build compilers, how to design programming languages. So this is sort of the broad area of systems, right? And then, of course, there is this whole big data revolution with machine learning and artificial intelligence, right? So these are things, you know, really beautiful questions, right? Like, can machines think? What, what can a machine do vis-a-vis what a human can do? Can a machine be creative, right? Like, uh, can I, uh, train a machine to have a conversation with me? So these are some really cool things that are happening in the world of machine learning these days. So these are sort of... There's a broad spectrum of things that you could, uh, study within computer science, and programming is only one sort of small tool that gets used by many people.

    5. UH

      Sure. So, um, very crude analogy would be that, um, in mechanical engineering, the people who build engines-

    6. SA

      Yeah

    7. UH

      ... are different from the people who drive the engines, because it's not the same thing.

    8. SA

      Yes.

    9. UH

      But I can understand that the engineers who are building the engines need to know how to drive it, and would be good at driving it, because that's t- those are the people who have built it.

    10. SA

      So I think the field is broad enough that this is not necessarily true. So in, uh... What I mean is, so for instance, I work in theory, right, and I work in cryptography. Now, it's not necessary that I know how to do programming in a certain programming language for a certain... You know, for building a compiler, let's say. So I have a working knowledge of the whole field. I have an in-depth knowledge of, you know, some part of the field, and it is not that I can do everything, even in terms of using. Like, uh, I, I cannot necessarily code in every programming language, uh, for example.

    11. UH

      Okay, so there's theoretical computer science, which is about algorithms and cryptography, and I guess it's a lot-

    12. SA

      Complexity, like hardness of problems.

    13. UH

      Okay, hardness, complexity.

    14. SA

      Yeah.

    15. UH

      And then there is, uh, systems, which is-

    16. SA

      Systems, yeah

    17. UH

      ... operating system, compilers-

    18. SA

      Networks, you know, computer networks, programming-

    19. UH

      How to realize them.

    20. SA

      Yeah, they-

    21. UH

      Sure.

    22. SA

      Yeah.

    23. UH

      Okay.

    24. SA

      And machine learning and so on, which is, you know, a third big field, sub-field.

    25. UH

      Of course, the field of machine learning has exploded in the last, uh-

    26. SA

      That's true

    27. UH

      ... maybe 10 years.

    28. SA

      Yeah, yeah.

    29. UH

      I remember we, we spoke to Professor Ravindran, um, a few weeks back, a few months back, and he was saying that his first course in reinforcement learning had six students, uh, 20 years back, and now it has, like-

    30. SA

      600. [chuckles]

  22. 34:3137:56

    Why Study Computer Science?

    1. UH

      What are the exciting things that are happening, and what should their proclivities be? Like, how will I know I'm- I'll say I'm interested in it. First of all, why should I be interested? And if I'm interested in it, what should I already be good at to excel in the field?

    2. SA

      Right. So I think that, uh, the answer, of course, depends on what level you're talking about, so let's start with undergrad. I mean, so I think, you know, in IIT, it's easy to know if you're good at it, because even to get admitted, you have to, uh-

    3. UH

      Cross the-

    4. SA

      ... cross the joint entrance exam. So you will pretty much only get here if you're good at it. But, uh, why should I be interested in it is actually an excellent question, and it's something that I think that, uh, students and, and their families don't ask enough. Because maybe we're still kind of in this mentality, by and large, where, you know, we're just thinking of what school, you know, or what'll get us a high-paying job, and, and, uh, things of this kind. Whereas, you know, maybe you could be, uh, a very good writer or, you know, a very good, um, biologist, right, or, uh, a very good architect. So I mean, there are lots and lots of, uh, fields of study, like history, right, uh, psychology. I mean, each field is very rich in its own way, and, uh, what you're interested in ideally should, uh, get determined by your very individual nature and, you know, uh, a, a good exposure to a wide variety of different topics. And ideally, like, there should be, like, an informed choice that is made. Of course, this does not happen, but, uh-

    5. UH

      Sure

    6. SA

      ... that's the ideal scenario. And, uh, in terms of why should I care, I mean, if you're good at it, first of all, I think that's good enough reason to care. But, uh, also additionally, you know, it's a great place to be. I mean, computer science is a very young field. There are lots of deep questions. Many of them are still open, right? And like cryptography, the field in which I work, I just can't think of a more fun field to be in, because, you know, it... There's just so many really exciting questions, and-... I feel like so many big discoveries are happening now, and, you know, we can be part of that. So it's, it's just a great time. So I think that whether undergrad or post-grad, you know, this kind of thought process, uh, has to be there. Uh, at the post-grad level, of course, it's much more, uh ... The hope is that, you know, it's much more informed. If, if you did your undergrad in whatever field was right for you, right? Then you've had an exposure to, uh, topics that might be potentially interesting to you, and then the post-grad is a place to kind of, uh, go deeper into something and kind of really get to business.

    7. UH

      Nice. So you're saying that at an undergrad level, of course, I mean, other than the fact that JEE sort of forces you to pick subjects, you should be aware of what your interest levels are?

    8. SA

      Yeah. I mean, in India, we're still quite far from that, but ideally, yes. I mean, there are students, you know, we see who basically, uh, are in the institute and, you know ... For instance, I had a, a student, I was her faculty advisor, and she just did not like it at all. Like, she did not like the course, she did not like the subjects. Uh, she wanted to do art, like, she wanted to be a writer. But there was so much pressure

  23. 37:5639:42

    Pressures on Computer Science Students

    1. SA

      on her to kind of stick on and, you know, she was trying to get out of the program. And her, uh, her parents would call me and say, "Ma'am, please don't let her quit," like, "You're her faculty advisor." And I would be like, "You know, i- this is not fair. Like, you need- she's an adult, and she needs to make her, her decisions." So-

    2. UH

      Would it be fair to say that computer science students, in particular, are under a lot of pressure, uh, because they've got here, [laughs] and then, you know, there's this, like, their expectation from-

    3. SA

      Pressure cooker.

    4. UH

      Yeah.

    5. SA

      Yeah. It, it is hard. I think it ... Yeah, it, it is, uh-

    6. UH

      Different kind of hard problem. [laughs]

    7. SA

      [laughs] It's a different kind of hard problem.

    8. UH

      How is, uh-

    9. SA

      But one that you want to solve.

    10. UH

      I know. I agree. Uh, and, um, maybe with the one thing that I do like about the IIT Madras system now is that, uh, at the very least, there are electives students can, uh, if they want, and if they allow themselves, can explore, uh, beyond their chosen field.

    11. SA

      Yeah, for sure you can explore. I mean, uh, having said that, in India, we still, uh, don't have that many very strong universities, you know, where you can really explore an undergrad, not just in ... Like, I, I didn't come in just to do engineering, you know? I, I came in, and now I'm exploring.

    12. UH

      Yeah.

    13. SA

      So that's a dream-

    14. UH

      Fair enough

    15. SA

      ... that's a dream setting.

    16. UH

      The memory that comes-

    17. SA

      We're still a technology, right, college, right? So we still-

    18. UH

      Memory that comes to my mind is that, uh, you know, there's one place where Steve Jobs talks about how he had explored a calligraphy class.

    19. SA

      Yeah, and that, yeah-

    20. UH

      That him design the first fonts for Mac.

    21. SA

      Yes, yes, yes.

    22. UH

      Uh, that's maybe quite- that, that kind of broad exposure-

    23. SA

      Absolutely

    24. UH

      ... is quite limited.

    25. SA

      Yeah.

  24. 39:4241:38

    India's Position in Cryptography

    1. UH

      Can I ask you, in the field of cryptography, um, where is, uh, India placed in the world, uh, or IIT Madras placed in the world? And maybe you can give us some sort of historical context. How has it moved in the last 10 years?

    2. SA

      Yeah, so 10 years is a good number that I can talk about, because I moved back to India about 10 years ago after my PhD and post-doc, and, uh, I think the field has come a very, very long way since then. So when I moved back, uh, there was almost nobody really, uh, working in public key cryptography. So there was, you know, ISI, which was doing some symmetric key crypto, but, like, in the, uh, public key space, there was almost nobody. And, uh, now in the last 10 years, actually, we have a, a very strong body of, uh, cryptographers. So for instance, you know, our top, uh, global venues for publishing kind of new, new research discoveries, right? Uh, these are like crypto and neurocrypt. And, uh, when I moved back, there was almost no representation that India had in, in these, uh, venues. And now if you look at it, actually, you know, every year at, at these top venues, we have a pretty good representation from India. So more people have not only come back, more strong people, but also they have trained their own students, right? So now we're actually generating strong cryptographers who are in turn contributing to the field from within the country. So it's, it's a really very positive movement. And at the time that I came back, you know, it, it was, it was just an idealism and a kind of a dream, but I, I really feel grateful that, uh, in fact, it, it, it has, uh, it ha- it has been so good. Like, I, I did not really know what to expect. I just knew I wanted to contribute to this. But in fact, it, it's been so fantastic, I r- I really feel very happy and proud that-

    3. UH

      I want to inquire more about the why you came back, but before that, right now you are leading a center called CyStar?

  25. 41:3842:28

    CyStar Center

    1. SA

      Yeah, I'm co-leading it.

    2. UH

      Okay.

    3. SA

      So there are three of us, uh, that lead different aspects of it. I lead the theory aspect. So CyStar, uh, there's a joke about the name, which I'll tell you in a second, but, uh, CyStar is, you know, a, a center for, uh, security- cryptography, security, trust, and reliability. That's the name.

    4. UH

      Okay.

    5. SA

      Uh, the internal joke is that I was pushing for this name because, you know, psi star, like, uh, psi star is the conjugate of the wave function. So psi is a Greek symbol. Anyway, this is an internal quantum joke, which I did not tell anybody, but this is why I was pushing for this name, and, uh, I'm very happy that everyone agreed to this name. So yeah, but, uh, coming back to CyStar, geeky jokes apart, [laughs] uh, it's, uh, it's a center of excellence in cybersecurity. And, uh,

  26. 42:2846:40

    CyStar as a Cybersecurity Focused Center

    1. SA

      you know, we're, we're just very, very happy and excited about it, because it's, uh, something that's finally kind of letting us come together and do more than a sum of the parts, you know? So, uh, there's three of us that- whose vision it, it's, uh, sort of, uh, driving, right?... uh, it started as like a small research project that, uh, we applied for, my colleague John and I. And then, uh, it's grown in sort of phase two of the funding, our, uh, sort of third partner, Chester, joined. There's also other affiliated faculty, of course. But the three of us look at different aspects of cybersecurity. So Chester looks at, you know, very applied, like implementation, you know, leakage attacks, so the applied side, which is directly society-facing. And, you know, uh, unsurprisingly, I think like, uh, that gets kind of the most, uh, excitement gets generated around this because-

    2. UH

      Because the, the, it's a near-term-

    3. SA

      Yeah

    4. UH

      ... impact.

    5. SA

      It's near-term impact, and it's also more relatable. And then I work, of course, on the algorithms, and that's also the, the vertical that I lead, so this is, uh, we've already talked about. And then John is, uh, you know, the bridge, so he leads this, you know, a kind of defining, uh, a theoretical models coming from the real world or applying theoretical ideas to real-world problems. So he's really like a distributed computing guy who understands aspects of both and makes connections between them. So we, we now have this... And, and we've, uh, hired, you know, s- uh, some amazing staff, like administrative staff, which helps us to do a lot of outreach, uh, you know, hackathons, winter schools, you know, startup incubators. And, uh, it helps us to pay our PhD students more. You know, we're hoping it will help us to attract more students. Uh, so it's, it's just a way to sort of really, um, dream big and, and, uh, put those dreams-

    6. UH

      Interesting

    7. SA

      ... on paper.

    8. UH

      The, the other centers that are there in, in IITM are sort of like, um, more, um... I, I don't know if this is true, but you can correct me, are interdisciplinary, have a broader focus. Here, it's a clear, focused effort around, uh, cryptography, right?

    9. SA

      Not cryptography, cybersecurity.

    10. UH

      Cybersecurity.

    11. SA

      So cryptography is just the map of cybersecurity, right? There's a lot more. I mean, there's systems building, right? So there's systems, there's applications, so that's actually the point of the center. And cybersecurity is so wide that already, like, we cannot do all the things that cybersecurity is. So for example, you know, we don't have people whose expertise is, you know, uh, machine learning for cybersecurity. So there- I'm trying to say it's a very wide field, right? Theory is, is one, uh, aspect of it. There's a lot more. And, uh, as a center, what we're doing is, you know, we're, we're trying to play to our strengths, so we don't claim to do everything. But we're trying to take a holistic view of the problem of cybersecurity, bring in all these different perspectives, and try to implement, uh, a vision, like I said, which sort of spreads the excitement, the awareness, you know, tries to really build experts for the country.

    12. UH

      Nice. Since you shared the, the internal story behind CyStar, I want to share something with you.

    13. SA

      Yeah.

    14. UH

      Uh, when CFI was set up, um, maybe about 15 years back, I was involved in the naming.

    15. SA

      Oh.

    16. UH

      And, uh, CFI is called like that also because the number phi, the Greek letter phi, is used, uh, as a letter for phase, and C is the letter used for constant.

    17. SA

      Oh. [chuckles]

    18. UH

      So it's constant phase, and the assumption is that it's a lead, uh, it's a leading angle, so it's a constant phase lead, so C phi. Uh, but we needed to sort of integrate it with-

    19. SA

      Yeah

    20. UH

      ... everybody understands, so CFI.

    21. SA

      So similar to CyStar.

    22. UH

      Yeah.

    23. SA

      [laughs]

    24. UH

      Nice. Professor, we spoke a lot about the work you're doing and the field itself. Um, but also I have seen you, uh, in various forums

  27. 46:4049:02

    Women in STEM

    1. UH

      talk about women in STEM. Would you like to sort of share your opinion and your views on that?

    2. SA

      So I mean, is there a particular question that you have?

    3. UH

      I don't know. I feel like sometimes I'm so removed from this topic, uh, also because technically I'm, I'm a marketing person, not even in STEM, right? Um, I'm not even aware of the challenges. I broadly know that there are fewer women in STEM than men.

    4. SA

      Yeah, I think actually in India, the numbers are not as, uh, skewed as, uh, even in some developed countries. I think the challenges in India are quite different than, for instance, in the US, where I, uh, did a lot of my education. Uh, I think in India, the challenge is not that at the undergrad level people think that women cannot, uh, you know, go into STEM. Uh, in fact, we have lots of girl students. Maybe not at the IITs. That's a different, uh, problem. I mean, that's because the IITs are not co-located with homes, right? There, there are few in the country, and, uh, students have to travel to-

    5. UH

      It's a residential.

    6. SA

      It's a residential college. So yeah, parents of girl students, uh, are often much more reluctant to send, uh, their daughters. Of course, this is changing quite fast, but, uh, overall-

    7. UH

      Not just the IITs are residential, but the, the... maybe some of the coaching institutions and-

    8. SA

      Yeah, right, right

    9. UH

      ... the-

    10. SA

      Leading up to it, yeah.

    11. UH

      They're also residential-

    12. SA

      Yeah

    13. UH

      ... they're also, are difficult.

    14. SA

      Right, right. So, uh, y- yeah, but I think that, you know, in un- at the undergrad level in general, we do see a lot of girl students. The problem is more that they sort of, uh, start... This, this crowd thins more and more as you go up, and this is due to kind of various reasons, you know, um, many of them are social, cultural, you know, perception issues, uh, and so on. So, uh, there are certainly challenges for women in STEM, and, uh, this is well documented. Like, so there is, for example, you know, this, uh, uh, famous experiment which was conducted several years ago called the head of the table experiment. And in this, uh, experiment, like-... there's a picture, there are two pictures where, uh, there's a table, and there are a bunch of people sitting around this table.

  28. 49:0251:35

    Performance of Women in STEM

    1. SA

      And, uh, there's one chair which is clearly the head chair, and, uh, in one version of the picture, there's a guy sitting in that, and in one version of the picture, there's a girl sitting in that. And then, uh, these pictures are shown- one of these two pictures, uh, is, is shown to, you know, random people, and, uh, they are asked, "Who do you think is leading this discussion?" So when there's a guy in the head chair, right, 100 out of 100 everyone points to him. When there's a woman, 50 out of 100, they point to the guy next to her. So this is actually a very old study, but I would not say that this has changed very much. I think that particularly seeing women in leadership positions is something that society still grapples with. And, you know, you face resistance in un- unexpected quarters. Because I think that even though now more and more women are accepted as part of the workforce, right, um, I think the litmus test is really disagreement. Like, there is kind of a template of... See, this is all, you know, very subjective, but this is my, uh, perception of, of the issue.

    2. UH

      Can you give me a minute?

    3. SA

      Yeah.

    4. UH

      Let me just, let me just rephra- let me just sort of mirror what you have said so far, uh, and understand it right. So if I understood right, what you're saying is that at a school/college level, there's not much difference, uh, in, say, in science grades, then-

    5. SA

      No, no, grades actually, you know, women do very well in the grades. The point is whether they continue-

    6. UH

      Continue.

    7. SA

      Yeah, yeah.

    8. UH

      Right, so it's-

    9. SA

      So did you know that in the IITs, the women students have a full point higher CGPA on average?

    10. UH

      Is it?

    11. SA

      Yes.

    12. UH

      Okay.

    13. SA

      This is a study done in IIT Delhi.

    14. UH

      Okay. So at a, at a IIT level, maybe there are about 20, 20 to 30% women, and then as you grow higher in STEM, it sort of thins down.

    15. SA

      Yeah.

    16. UH

      At, at a leadership level, it's very less.

    17. SA

      Very, very less, yeah.

    18. UH

      And Professor Preeti, of course, now is recognized as-

    19. SA

      Yes

    20. UH

      ... IIT's first IIT woman director.

    21. SA

      Yes. Yeah.

    22. UH

      And that-

    23. SA

      And I'm really proud of this, and Preeti is amazing, and, you know, I'm really happy that, uh, she's doing this. And also, you know, that, uh, we're doing this, that IITM did this. It's really amazing.

    24. UH

      Yeah. Hopefully we'll get to interview her also [chuckles] soon, when she is here, but she's mostly in Zanzibar now.

    25. SA

      Yeah, right.

    26. UH

      Um, the other thing that, and just to sort of lead into the last segment of this interview, um, at some point, uh, we spoke about it, but let's come back to it. Um, your

  29. 51:3555:38

    Shweta's Personal Journey

    1. UH

      own personal journey, um, leading to a decision of, uh, your returning from US, uh, can we inquire a little bit about that? Maybe you could start with your undergrad or your schooling days, or, you know, your-

    2. SA

      Well, so I feel like my whole life journey, uh, is really different from how I see undergrads now, in the sense that when I see many students now, uh, they have very clear ideas of, you know, what they want to do. Like, everything will be very well-researched, you know, where to join, what to do, you know, next steps, all the pros and cons will be weighed, and so on. Uh, in my particular case, I think that I never used to take anything super seriously, and I was just doing more of a random walk, like, uh, you know, just trying to explore things that I found interesting. In growing up, I felt that, um, there was a lot of struggle and strife that I saw around me. Like, I came from a middle-class background, and opportunities were pretty few and far between. Like, when I see, again, you know, my, uh... I see children of our colleagues here, right? So they have so much opportunity. But back in our generation, I mean, you know, we, we didn't really know much. We weren't exposed to much. And, uh, I just happened to do, you know, computer science because of... That was the pressure that, uh, you know, society placed on us. And, uh, there were various other things that I liked. I, I particularly loved art, and I was also very good at it, and, um, for a long time later, I felt like I should have been an artist. But luckily, I became a mathematician, and it's the same thing, so, you know, I'm, I'm happy where I am now. But, uh, yeah, I did my undergrad, and I didn't have really any exposure to research or anything. And at the time, I would be reading these, uh, popular science books, you know, by Stephen Hawking and, and so on. And there was this one particular book which really made an impact on me, and this was by Brian Greene, um, called The Fabric of the Cosmos. And, uh, I think back then, I mean, I was already in this, uh... I, I used to feel like, you know, s- something exciting is happening somewhere, and I just need to find it. And when I read this, uh, book, it was just so mind-blowing. Like, you know, there's a chapter where he explains general relativity in a way that, you know, an amateur can really understand. And, uh, I think that in some sense, that really, um, catalyzed my desire to find something, you know, uh, new and exciting. And, uh, I just did, like, a brute force application to a whole bunch of universities. Uh, I got selected, you know, I went abroad. Even when I did that for my master's and subsequently PhD, I was still exploring a lot of things. In fact, I took a lot of art classes. I took a lot of math classes. I took some physics classes. Of course, I took computer science classes. So I was just very excited to learn.

    3. UH

      On your post-grad in the US?

    4. SA

      Yeah, this is during my master's and, uh, and PhD. So cryptography also happened, you know, really by chance. Like, I was visiting a professor. It was supposed to just be a short-... one or two-week visit about some particular question, and he was a cryptographer. And, you know, uh, during that time I got exposed to cryptography. I just started exploring it, and eventually, I mean, you know, that, that's the field where I, uh, really committed myself. So, ah, this is how my, my education was.

    5. UH

      If, if the field of cryptography is, uh, at the time that you're talking about, maybe in the mid-2000, mid-2000s, early-

    6. SA

      '10. Yeah, 2010, let's say.

    7. UH

      If the, if the field of cryptography is more evolved in the US and, uh, wasn't so evolved here, um, what led for you to take the decision to come back?

  30. 55:3857:43

    Decision to Return to India

    1. SA

      So, uh, this, this was just a personal decision. Before I even left, I knew I would come back, and I was in fact 100% clear about it. Uh, and, uh, this is because, uh, as I said, you know, while growing up, uh, I had very little exposure, and I saw a lot of struggle around me, and, uh, uh, I really felt for it. And eventually I started feeling that, uh, you know, somehow in this developing country where opportunities are often so hard to come by-

    2. UH

      Yeah

    3. SA

      ... so scarce, you know, so many talented people don't find these opportunities. I felt that I was somehow profoundly fortunate to, to have received the best of what this environment could offer.

    4. UH

      And you used the word random walk, which by the way, is also a name of algorithm, right?

    5. SA

      Yeah. I mean, it's, it's... Yeah, it's, it's, uh, not one algorithm, but, like, a class of-

    6. UH

      Okay

    7. SA

      ... algorithms. But, uh, yeah, so I, I really, uh, cared about coming back to this environment and giving back whatever I could. So even before I left, it was clear to me that I would always come back, and as soon as I finished my education... Uh, during my education, I also met my husband and, my now husband, and, uh, he had the, the same dream. And so as soon as we finished our education, we j- we just came back, so we did not even apply for jobs there. And I, I mean, uh, I'm very happy with this decision. No regrets.

    8. UH

      And we are lucky for it. [chuckles]

    9. SA

      Yes. [laughing]

    10. UH

      I think you m- you mentioned, uh, maybe, uh, we, we can probably stop here, but we'll just explore it and see if it works, and then we- otherwise we'll cut it off. Um, Professor, uh, during our preparation, you also mentioned, uh... And I, I just want to note that, uh, to the audience, that the preparation for this interview was the hardest, because I know nothing about cryptography, and my research team also knows nothing, so we actually ended up learning a lot. Professor, during our preparation, you also used the word Atma Shraddha. I, I love the way you explained

  31. 57:431:03:23

    Atma Shraddha (Self-Belief)

    1. UH

      it. Um, could you do it for our audience?

    2. SA

      Well, it was in the context of something we were discussing. I, I feel that, uh, you know, we have everything that it takes. Uh, like, we as a country, we as an institute, we have everything that it takes. We have all the potential, but we're still not where we need to be, like, you know, in science, for instance, right? So, uh, I feel that this is because somehow we're still in this post-colonial mindset where we feel that we need to go out to be cool, like to be, you know, to, like, to be part of the excitement or, or, uh, something. And by the way, as I already, uh, said, even when I was a young person, you know, um, I, I did go abroad. At that time, uh, these opportunities were not there. Like, now, you know, things are significantly better. There are a lot of groups here that are on par with, like, you know, the, the best in the world. But still we, we see that students many times, you know, they just want to get out. This is not based on any fact or any, you know, objective measure of, you know, the scientific output or the funding opportunities, or even the salaries, which, you know, can be very good now. But it's just based on this perception that, you know, this place, this country is not g- good enough. I, I just need to get out.

    3. UH

      Sure, and, you know-

    4. SA

      So this is... Yeah.

    5. UH

      Y- you're from a science field, but i- in my field, in marketing, I've seen people who have worked with us for one or two years, and they apply to MBA colleges abroad. And I'm looking at the college they have applied to, and I know it's not a great college, and I have zero idea why they're going.

    6. SA

      Yeah, I feel the same way. Like, uh, for instance, I have a course on quantum algorithms on NPTEL, which is our, uh, in-house MOOC, as you know. And, uh, you know, there are students who see this course and who write in to me about it. And I won't name, uh, any specifics but, like, you know, recently there's a student sitting in, uh, you know, university X in country Y, and, uh, writing to me saying that, "You know, I'm trying to work on this topic, and I've been watching your lectures, and can you guide me in blah and blah?" And this student has done his undergrad at a very good college in India, right? And has gone to this university X in country Y, right, which is nowhere compared to our own group here or other groups in India. Like, I'm not talking only of IIT Madras or, you know, our particular CyStar or something, but I mean, we have some fantastic, uh, researchers in India, right? But students, they... I- this is where I was using the word Atma Shraddha. I feel that we lack this, uh... You know, as a people, we're still not fully there. We, we imagine that we don't have it by ourselves. Like, we, we need to get out, and only then we'll find what we want.

    7. UH

      And I want to share another story on this. Uh, one, one of the times, uh, as part of our work with Ask IITM, one of the times where we were doing a sort of a, a, a sort of a roadshow, you could say, uh, in Bangalore, and we were describing all this work that students do here, all the work that professors do, and we were talking about the Hyperloop project that happens here, and-... I think there was like a sort of a cognitive dissonance with the audience, because the audience was in the mindset that this kind of work happens only, you know-

    8. SA

      Abroad.

    9. UH

      Abroad. Only Elon Musk-

    10. SA

      This exactly what I mean by Atma Shraddha. You see, that, you know, you, you cannot, because you yourself relate to other Indians, right? And you don't have confidence or faith in yourself, you imagine that nobody here can be doing anything, uh, you know, really top class or important or, or whatever. So you don't even believe it.

    11. UH

      Yeah.

    12. SA

      Like, if we tell students, right, that, ah, for example, look at objective measures of, uh, yeah, f- for instance, in research, right? There are measures like citations or whatever, like diff- for different fields it could be different. But they're not even at the place where they can start evaluating this question, because there, there is such a strong preconceived notion that I/we cannot do it, you know? Or at least cannot do it here. So maybe I can do it if I ship abroad and, you know, it doesn't matter even where. Like, so by the way, it's not that I'm saying you should always be here, right? You need to make an informed choice, like, what are your options, uh, you know, what, what are you good at? Like, what is the group good at, et cetera. So just an informed choice, and indeed, it could be the case that some institute abroad i- is better for you for your interests. That could certainly be the case, but it's not a given.

    13. UH

      Yeah, it's not universally true.

    14. SA

      It's not universally.

    15. UH

      And, and-

    16. SA

      Yeah

    17. UH

      ... for specific research areas, uh, we are actually doing really well.

    18. SA

      We're doing very well, yeah.

    19. UH

      And it would be almost, like, silly, I, I'm using a mild word, silly for students to assume that they have to go abroad, and-

    20. SA

      It's not just students, right? I mean, it, it's our larger population. Like, uh, we, we need to develop more confidence in ourselves, I think. Yeah, more Atma Shraddha. [chuckles]

    21. UH

      More Atma Shraddha, and hopefully, uh, through these podcasts, uh, we can establish that. This is really the Best Place To Build. Thank you so much. But before we leave, uh, I wanna ask you if you are, um, still doing

  32. 1:03:231:05:00

    Art and Cryptography

    1. UH

      art? I know you said that you do it as a student, uh, if that's something you do as an adult.

    2. SA

      Absolutely.

    3. UH

      And, uh-

    4. SA

      I do, I do do art, and, uh, uh, as I said, you know, cryptography itself is a kind of art. So in fact, I would say cryptography is a lot like abstract expressionism, which is one of my favorite movements in art. Because if you see abstract expressionist paintings, right, they are also a, a dance between structure and randomness, and that's exactly cryptography.

    5. UH

      Oh, that's beautiful.

    6. SA

      [chuckles]

    7. UH

      Because it's like I'm trying to tell you something, but not everybody will get it.

    8. SA

      Ah, no, no, no, it's actually... It's actually, you know, this, uh, this sort of, uh, dialogue between form and formlessness, you know? It, it's there, but it's not quite there. So I mean, of course, the cryptography is, is, uh, one example of this in, in my mind, but of course, like in the artistic movement, there can be many ways of expressing this idea. Cryptography, in my mind, is one of the ways. And, uh, more broadly, I think that these distinctions between fields are all, um, you know, unnecessary. Uh, like, what drew me to art is, is a love for, uh, a, a sort of pursuit of beauty, right? And, and this is also something that drives me in, in math or, you know, uh, helps me to appreciate music or poetry or history, I mean, psychology. So I mean, it's everywhere, like, uh, beauty is everywhere, and, uh, you just have to approach it.

    9. UH

      I think that's a great place to stop. Thank you for

  33. 1:05:001:05:44

    Conclusion

    1. UH

      joining us. Um, if you like this podcast, please like, share, and subscribe, uh, and, uh, follow, uh, Professor Shweta. Uh, if possible, view her NPTEL lectures, and, and join her in her courses. Thank you.

    2. SA

      Thank you so much. [upbeat music]

Episode duration: 1:05:44

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