Jan Hein van Dierendonck’s Painting of Shannon

Jan Hein van Dierendonck, a science writer and illustrator/cartoonist from Leiden, recently contacted the IT Society about an oil painting he made of Claude Shannon. He has kindly given permission to post it here. It will be used by some of the Shannon Centenary events this year.

Claude Shannon, by Jan Hein van Dierendonck

Claude Shannon, by Jan Hein van Dierendonck

Claude Elwood Shannon (April 30, 1916 – February 24, 2001)

In the Forties a juggling Claude Elwood Shannon rides a unicycle down the endless hallways of Bell Labs, a telecommunications research laboratory south of New York. Perhaps this balancing act puts his brilliant mind in the right state to look at complex problems in an original way and to devise the formulas that initiate the Digital Era.

As a 21-year-old master’s degree student at the Massachusetts Institute of Technology, Shannon wrote his thesis demonstrating that electrical applications of Boolean algebra could construct and resolve any logical, numerical relationship. In 1948 this mathematician, electronic engineer, and cryptographer published a landmark paper that laid the foundation for information theory. From that moment on, information is something computable. Whether you are dealing with images, text or sound: convert everything into zeros and ones and remove all redundant information and noise. This has changed our world completely. Without Shannon’s Information Theory, your phone simply wasn’t smart.

Averse to fame, the professor in electronics preferred tinkering with his amazing magnetic mouse in a maze with memory and his mechanic juggling robots. He also refined his Juggling Theorem: the number of hands (H) multiplied by the total time a ball spends in the air (F) and is held in a hand (D) is in balance with the number of balls (N) multiplied by the total time a hand is empty (V) and holding a ball (D).

On April 30, 2016, he would have been a hundred.

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Postdoc positions at UCLA in Coding Theory

I will write more about the IHP workshop! In the meantime, here are some exciting postdoc opportunities with my ex-classmate Lara Dolecek!

I’m writing to let you know that I have 2 postdoc positions available in my research group at UCLA, starting this summer. I am looking for talented students who want to work on one of the following:

  1. Coding theoretic methods and algorithms for emerging memories and modern computing systems
  2. New algorithms and coding-theoretic techniques for data management (data science)

Both projects are interdisciplinary. Postdocs will be working closely with me and a vibrant group of my graduate students, and will have the opportunity to collaborate with other researchers and to interact with our industry sponsors.

Strong background in mathematics and interest in interdisciplinary research are required.

I would greatly appreciate if you can pass this information to interested students.

Prospective students should contact me via email at dolecek@ee.ucla.edu

with subject line

[Prospective postdoc interested in LORIS research]

along with their CV and 3 selected publications. Students should plan to arrange for 2 letters of recommendation to be sent to the email address above.

IHP “Nexus” Workshop on Privacy and Security: Day 1

The view from my office at IHP

The view from my office at IHP

I am attending the Nexus of Information and Computation Theories workshop at the Institut Henri Poincaré in Paris this week. It’s the last week of a 10 week program that brought together researchers from information theory and CS theory in workshops around various themes such as distributed computation, inference, lower bounds, inequalities, and security/privacy. The main organizers were Bobak Nazer, Aslan Tchamkerten, Anup Rao, and Mark Braverman. The last two weeks are on Privacy and Security: I helped organize these two weeks with Prakash Narayan, Salil Vadhan, Aaron Roth, and Vinod Vaikuntanathan.

Due to teaching and ICASSP, I missed last week, but am here for this week, for which the sub-topics are security multiparty computation and differential privacy. I’ll try to blog about the workshop since I failed to blog at all about ITA, CISS, or ICASSP. The structure of the workshop was to have 4 tutorials (two per week) and then a set of hopefully related talks. The first week had tutorials on pseudorandomness and information theoretic secrecy.

The second week of the workshop kicked off with a tutorial from Yuval Ishai and Manoj Prabhakaran on secure multiparty computation (MPC). Yuval gave an abbreviated version/update of his tutorial from the Simons Institute (pt1/pt2) that set up the basic framework and language around MPC: k parties with inputs x_1, x_2, \ldots, x_k want to exchange messages to implement a functionality (evaluate a function) f(x_1, x_2, \ldots, x_k) over secure point-to-point channels such they successfully learn the output of the function but don’t learn anything additional about each others’ inputs. There is a landscape of definitions within this general framework: some parties could collude, behave dishonestly with respect to the protocol, and so on. The guarantees could be exact (in the real/ideal paradigm in which you compare the real system with an simulated system), statistical (the distribution in the real system is close in total variation distance to an ideal evaluation), or computational (some notion of indistinguishability). The example became a bit clearer when he described a 2-party example with a “trusted dealer” who can give parties some correlated random bits and they could use those to randomly shift the truth table/evaluation of f(x_1, x_2) to guarantee correctness and security.

Manoj, on the other hand talked about some notions of reductions between secure computations: given a protocol which evaluates f, can you simulate/compute g using calls to f? How many do you need? this gives a notion of the complexity rate of one function in terms of another. For example, can Alice and Bob simulate a BEC using calls to an oblivious transfer (OT) protocol? What about vice versa? What about using a BSC? These problems seem sort of like toy channel problems (from an information theory perspective) but seem like fundamental building blocks when thinking about secure computation. As I discussed with Hoeteck Wee today, in information theory we often gain some intuition from continuous alphabets or large/general alphabet settings, whereas cryptography arguments/bounds come from considering circuit complexity: these are ideas that we don’t think about too much in IT since we don’t usually care about computational complexity/implementation.

Huijia (Rachel) Lin gave an introduction to zero-knowledge proofs and proof systems: a verifier wants to know if a statement X is true and can ask queries to a prover P which has some evidence w that it wants to keep secret. For example, the statement might be “the number y is a perfect square” and the evidence might be an \alpha such that y = \alpha^2 \mod n. The prover doesn’t want to reveal w = \alpha, but instead should convince the verifier that such an alpha exists. She gave a protocol for this before turning to a more complicated statement like proving that a graph has a Hamiltonian cycle. She then talked about using commitment schemes, at which point I sort of lost the thread of things since I’m not as familiar with these cryptography constructions. I probably should have asked more questions, so it was my loss.

Daniel Wichs discussed two problems he called “multi-key” and “spooky” fully-homomorphic encryption (FHE). The idea in multi-key FHE is that you have N users who encrypt values \{ x_i : i \in [N] \} with their public key and upload them to a server. Someone with access to the server wants to be able to decode only a function f(x_1, x_2, \ldots, x_N) using the combined private keys of all the users. In “spooky” FHE, you have N decoders, each with one of the private keys, but they want to decode values \{y_i : i \in [N]\} which are functions of all of the encoded data. A simple example of this is when y_1 \oplus y_2 = x_1 \wedge x_2: that is, the XOR of the outputs is equal to the AND of the inputs. This generalizes to the XOR of multiple outputs being some function of the inputs, something he called additive function sharing. He then presented schemes for these two problems based on the “learning with errors” from Gentry, Sahai, and Waters, which I would apparently have to read to really understand the scheme. It’s some sort of linear algebra thing over \mathbb{Z}_q. Perhaps there are some connections to linear block codes or network coding to be exploited here.

Salim El Rouayheb’s Shannon Channel: Pulkit Grover at 1300 EST

Salim El Rouayheb has started an exciting new initiative inspired by the TCS+ series. TCS+ is a seminar series on theoretical computer science (plus more) given over Google Hangout so that people across the world can attend the talk (and even ask questions). Nobody has to travel anywhere. Salim’s version is for information theory and he’s calling it Shannon’s Channel. If you’re interested in getting announcements you can sign up for the mailing list.

Salim told me about this at Allerton and I meant to plug it here on the blog earlier but then the semester plus excessive travel ate me. He just sent a reminder yesterday that the inimitable Pulkit Grover will be giving a seminar today (Monday) at 1 PM:

Error-correction and suppression in communication and computing: a tradeoff between information and energy dissipation

Abstract: Information naturally tends to dissipate. This dissipation can be slowed down, but this requires increased energy dissipation. Shannon’s capacity theorem can be interpreted as the first word in this information-energy dissipation tradeoff, but it barely scratches the surface. I will begin with a survey of recent results on minimal energy dissipation for reliable information communication. I will discuss how incorporating energy dissipated in transmitter/receiver circuitry as well as in transmission leads to radically different fundamental limits on information-energy interactions than those obtained by Shannon. I’ll also talk about practical applications in short distance wired and wireless communications.

These techniques can also be applied to obtain fundamental limits to information-energy dissipation for reliable computation using unreliable/noisy components (first considered in [von Neumann ’56]). Recent work on strong data-processing inequality points out the fundamental difficulty in noisy computing: information-dissipation across multiple computation steps. We ask the question: what is the minimum energy-dissipation needed to keep information intact (reliability constant) as the computation proceeds? I’ll describe our novel ENCODED strategy (ENcoded COmputation with DEcoders EmbeddeD) for linear computations on noisy substrates, that outperforms uncoded/repetition-based strategies and keeps error-probability bounded below a constant. The key insight is that for computing in noisy environments, repeated error-suppression (that dissipates energy) is essential to keep information from dissipating. Application to emerging devices and circuit design techniques will also be discussed.

Finally, I’ll talk about a high-density noninvasive biopotential sensing problem, which is closely related to the problem of compressing a Markov source distributedly. Here, energy constraints limit the number of sensors. I’ll discuss how a novel “hierarchical” architecture that contains error-accumulation turns out to have a substantially improved energy-information dissipation tradeoff than simply “compressing innovations” (a strategy known to be suboptimal from a work of Kim and Berger).

The Hangout link is here and the talk will be on YouTube afterwards.

Unfortunately, I have to teach during that time, otherwise I would totally be there, virtually.

Mathematical Tools of Information-Theoretic Security Workshop: Days 2-3

I took sketchier notes as the workshop progressed, partly due to the ICASSP deadline, but also because jet lag started to hit me. The second day was a half day, which started with Zhenjie Zhang giving a tutorial on differential privacy from a databases/data mining perspective and my talk on more machine learning aspects. In between us was a talk by Ben Smyth on building automatic verification for security protocols. Basically you write the protocol as a program and then the ProVerif verifier will go and try to break your protocol. As an example, it can automatically find/generate a man-in-the-middle attack if one exists. I thought it was pretty neat, especially after having recently talked to someone about automatic proof systems. It’s based on something called the applied pi calculus, which I did not understand at all, but hey, I learned something new, which was great. The last two talks of the day were by Lalitha Sankar and Mari Kobayashi. Lalitha talked about mutual information based measures of privacy leakage in an interactive communication setting that is the information-theoretic analogue of communication complexity models in CS. Mari talked about the broadcast channel with state feedback. This is trying to find secure analogues of these opportunistic multicast settings where you need to also generate a secret key.

The last day was on quantum! I learned a lot and took few notes, unfortunately. Andreas Winter gave a tutorial on quantum (the slides for most talks are online and his are as well) and Ciara Morgan discussed the challenges in proving a strong converse for the the capacity of quantum channels. Damian Markham talked about secret sharing in quantum systems. Masahito Hayashi gave a very densely-packed talk surveying a large number of results based on secure randomness extraction and hash functions using Rényi information measures. I think privacy amplification is really interesting but I think I need a tutorial on it before I can really get the research results. The last non-overview talk I have notes on was by David Elkouss (apologies to the remaining speakers): this was a really interesting presentation on how to decide which of two channels is better from a quantum communication sense. The slides are a little engimatic, but the papers are online.

Shlomo Shamai made it to the last day of the workshop (the intersection with High Holidays was unfortunate) — he talked about the layered secrecy view of the broadcast channel: rather than thinking only of the secret message as carrying information, one can think of certain layers (c.f. superposition coding) as being secured based on the channel to the non-legitimate receiver. For example, in a degraded broadcast channel, the strong receiver’s message can sometimes be thought of as secret from the weak receiver. This leads to a raft of models and setups based on who wants to keep what secret from whom, shedding some light on standard superposition, rate splitting, binning, and embedding constructions. The talk was largely based on a paper in the current issues of the Proceedings of the IEEE.

All in all, this was a really great workshop, and the organizers were very generous in the organization.

Mathematical Tools of Information-Theoretic Security Workshop: Day 1

It’s been a while since I have conference-blogged but I wanted to set aside a little time for it. Before going to Allerton I went to a lovely workshop in Paris on the Mathematical Tools of Information-Theoretic Security thanks to a very kind invitation from Vincent Tan and Matthieu Bloch. This was a 2.5 day workshop covering a rather wide variety of topics, which was good for me since I learned quite a bit. I gave a talk on differential privacy and machine learning with a little more of a push on the mathematical aspects that might be interesting from an information-theory perspective. Paris was appropriately lovely, and it was great to see familiar and new faces there. Now that I am at Rutgers I should note especially our three distinguished alumnae, Şennur Ulukuş, Aylin Yener, and Lalitha Sankar.

Continue reading

ISIT 2015 : statistics and learning

The advantage of flying to Hong Kong from the US is that the jet lag was such that I was actually more or less awake in the mornings. I didn’t take such great notes during the plenaries, but they were rather enjoyable, and I hope that the video will be uploaded to the ITSOC website soon.

There were several talks on entropy estimation in various settings that I did not take great notes on, to wit:

  • OPTIMAL ENTROPY ESTIMATION ON LARGE ALPHABETS VIA BEST POLYNOMIAL APPROXIMATION (Yihong Wu, Pengkun Yang, University Of Illinois, United States)
  • DOES DIRICHLET PRIOR SMOOTHING SOLVE THE SHANNON ENTROPY ESTIMATION PROBLEM? (Yanjun Han, Tsinghua University, China; Jiantao Jiao, Tsachy Weissman, Stanford University, United States)
  • ADAPTIVE ESTIMATION OF SHANNON ENTROPY (Yanjun Han, Tsinghua University, China; Jiantao Jiao, Tsachy Weissman, Stanford University, United States)

I would highly recommend taking a look for those who are interested in this problem. In particular, it looks like we’re getting towards more efficient entropy estimators in difficult settings (online, large alphabet), which is pretty exciting.

QUICKEST LINEAR SEARCH OVER CORRELATED SEQUENCES
Javad Heydari, Ali Tajer, Rensselaer Polytechnic Institute, United States
This talk was about hypothesis testing where the observer can control the samples being taken by traversing a graph. We have an n-node graph (c.f. a graphical model) representing the joint distribution on n variables. The data generated is i.i.d. across time according to either F_0 or F_1. At each time you get to observe the data from only one node of the graph. You can either observe the same node as before, explore by observing a different node, or make a decision about whether the data from from F_0 or F_1. By adopting some costs for different actions you can form a dynamic programming solution for the search strategy but it’s pretty heavy computationally. It turns out the optimal rule for switching has a two-threshold structure and can be quite a bit different than independent observations when the correlations are structured appropriately.

MISMATCHED ESTIMATION IN LARGE LINEAR SYSTEMS
Yanting Ma, Dror Baron, North Carolina State University, United States; Ahmad Beirami, Duke University, United States
The mismatch studied in this paper is a mismatch in the prior distribution for a sparse observation problem y = Ax + \sigma_z z, where x \sim P (say a Bernoulli-Gaussian prior). The question is what happens when we do estimation assuming a different prior Q. The main result of the paper is an analysis of the excess MSE using a decoupling principle. Since I don’t really know anything about the replica method (except the name “replica method”), I had a little bit of a hard time following the talk as a non-expert, but thankfully there were a number of pictures and examples to help me follow along.

SEARCHING FOR MULTIPLE TARGETS WITH MEASUREMENT DEPENDENT NOISE
Yonatan Kaspi, University of California, San Diego, United States; Ofer Shayevitz, Tel-Aviv University, Israel; Tara Javidi, University of California, San Diego, United States
This was another search paper, but this time we have, say, K targets W_1, W_2, \ldots, W_K uniformly distributed in the unit interval, and what we can do is query at each time n a set S_n \subseteq [0,1] and get a response Y_n = X_n \oplus Z_n where X_n = \mathbf{1}( \exists W_k \in S_n ) and Z_n \sim \mathrm{Bern}( \mu(S_n) + b ) where \mu is the Lebesgue measure. So basically you can query a set and you get a noisy indicator of whether you hit any targets, where the noise depends on the size of the set you query. At some point \tau you stop and guess the target locations. You are (\epsilon,\delta) successful if the probability that you are within \delta of each target is less than \epsilon. The targeting rate is the limit of \log(1/\delta) / \mathbb{E}[\tau] as \epsilon,\delta \to 0 (I’m being fast and loose here). Clearly there are some connections to group testing and communication with feedback, etc. They show there is a significant gap between the adaptive and nonadaptive rate here, so you can find more targets if you can adapt your queries on the fly. However, since rate is defined for a fixed number of targets, we could ask how the gap varies with K. They show it shrinks.

ON MODEL MISSPECIFICATION AND KL SEPARATION FOR GAUSSIAN GRAPHICAL MODELS
Varun Jog, University of California, Berkeley, United States; Po-Ling Loh, University of Pennsylvania, United States
The graphical model for jointly Gaussian variables has no edge between nodes i and j if the corresponding entry (\Sigma^{-1})_{ij} = 0 in the inverse covariance matrix. They show a relationship between the KL divergence of two distributions and their corresponding graphs. The divergence is lower bounded by a constant if they differ in a single edge — this indicates that estimating the edge structure is important when estimating the distribution.

CONVERSES FOR DISTRIBUTED ESTIMATION VIA STRONG DATA PROCESSING INEQUALITIES
Aolin Xu, Maxim Raginsky, University of Illinois at Urbana–Champaign, United States
Max gave a nice talk on the problem of minimizing an expected loss \mathbb{E}[ \ell(W, \hat{W}) ] of a d-dimensional parameter W which is observed noisily by separate encoders. Think of a CEO-style problem where there is a conditional distribution P_{X|W} such that the observation at each node is a d \times n matrix whose columns are i.i.d. and where the j-th row is i.i.d. according to P_{X|W_j}. Each sensor gets independent observations from the same model and can compress its observations to b bits and sends it over independent channels to an estimator (so no MAC here). The main result is a lower bound on the expected loss as s function of the number of bits latex b, the mutual information between W and the final estimate \hat{W}. The key is to use the strong data processing inequality to handle the mutual information — the constants that make up the ratio between the mutual informations is important. I’m sure Max will blog more about the result so I’ll leave a full explanation to him (see what I did there?)

More on Shannon theory etc. later!