Title
Clustering and Data Anonymization by Mutual Information

Abstract
In this talk, we first introduce  the Shannon theoretic
multi-clustering problem and investigate its properties, uncovering
connections with many other coding problems in the literature. The figure
of merit for this information-theoretic problem is mutual information, the
mathematical properties of which make the multi-clustering problem amenable
to techniques that could not be used in a general rate-distortion setting.
We start by considering the case of two sources, where we derive
singleletter bounds for the achievable region by connecting our setting to
hypothesis testing and pattern recognition recognition problems in the
information theory literature. We then generalize the problem setup to an
arbitrary number of sources and study a CEO problem with logarithmic loss
distortion and multiple description coding. Drawing from the theory of
submodular functions, we prove a tight inner and outer bound for the
resulting achievable region under a suitable conditional independence
assumption. Furthermore, we present a proof of the well-known two-function
case of a conjecture by Kumar and Courtade (2013), showing that the
dictator functions are essentially the only Boolean  functions maximizing
mutual information.  The key step in our proof is a careful analysis of the
Fourier spectrum of the two Boolean functions. Finally, we study
information-theoretic applications to the problem of statistical  data
anonymization via mutual information and deep learning methods in which the
identity of the data writer must remain private even from the learner.

Joint works with Dr. Georg Pichler (TU Wien, Austria), Prof. Gerald Matz
(TU Wien, Austria),  Clément Feutry (CentraleSupélec, France) and Yoshua
Bengio (Montréal, Canada)

Short biography
Pablo Piantanida received both B.Sc. in Electrical
Engineering and B.Sc. in Mathematics degrees from the University of Buenos
Aires (Argentina) in 2003, and the Ph.D. from Université Paris-Sud (Orsay,
France) in 2007. Since October 2007 he has joined the Laboratoire des
Signaux et Systèmes (L2S), at CentraleSupélec together with CNRS (UMR 8506)
and Université Paris-Sud, as an Associate Professor of Network Information
Theory. He is an IEEE Senior Member, coordinator of the Information Theory
and its Applications group (ITA) at L2S, and  coordinator of the
International Associate Laboratory (LIA) of the CNRS “Information, Learning
and Control” with several institutions in Montréal and General Co-Chair of
the 2019 IEEE International Symposium on Information Theory (ISIT). His
research interests lie broadly in information theory and its interactions
with other fields, including multi-terminal information and Shannon theory,
machine learning, statistical inference, communication mechanisms for
security and privacy, and representation learning.

To be announced

Abstract

As part of the internet of things (IoT), the number of sensor nodes that wish to communicate with each other has exploded and is expected to further increase dramatically. Such an increase of communication devices inherently leads to involved communication and hypothesis testing scenarios, and thus calls for new coding and testing strategies. The talk presents new strategies and corresponding error exponents for different network scenarios, and it proves information-theoretic optimality of the proposed strategies in some cases. Special attention is given to scenarios where information collected at a sensor is desired at multiple decision centres and where communication is multi-hop involving sensor nodes as relays. In these networks, sensors generally compete for network resources, and relay sensors can process received information with sensed information or forward intermediate decisions to other nodes. Depending on the studied error exponents, some of these intermediate decisions require special protection mechanisms when sent over the network. The talk is based on joint work with Sadaf Salehkalaibar, Roy Timo, and Ligong Wang.

Abstract

The downlink of a cloud radio accessnetwork (C-RAN) architecture can be modeled as a diamond network. The baseband unit (BBU) is connected to remote radio heads (RRHs) via fiber links that are modeled as rate-limited bit pipes. Bounds on the rates for reliable communication are evaluated for single-antenna RRHs. A lower bound is based on Marton’s coding, which facilitates dependence across the RRH signals. An upper bound uses Ozarow’s technique to augment the system with an auxiliary random variable. The bounds are studied over scalar Gaussian C-RANs and are shown to meet and characterize the capacity for interesting regimes of operation. The bounds are also evaluated for an abstract model: a noise-free binary adder channel (BAC). The capacity of the BAC is established for all ranges of bit-pipe capacities, which seems to yield a new combinatorial result on sum sets. This work is based on joint work with Shirin Saeedi Bidokhti and Shlomo Shamai.

Abstract

We connect the information flow in a neural network to sufficient statistics; and show how techniques that are rooted in information theory, such as the source-coding based information bottleneck method can lead to improved architectures, as well as a better understanding of the theoretical foundation of neural networks, viewed as a cascade compression network. We illustrate our results and view through some numerical examples.

Abstract

Body Impedance is an effective biometric because each human body
exhibits a unique response to a signal applied at the palm of one hand
and measured in the palm of the other hand. We will see how body
impedance can be used both as a traditional biometric; to generate
cryptographic keys for use in traditional security purposes; or for
device pairing.

Our device pairing scheme is based on the idea that two devices can
pair, if they are physically held by the same person (at the same
time). To pair two devices, a person touches a conductive surface on
each device. While the person is in contact with both devices, the
human body acts as a transmission medium for intra-body communication
and the two devices can communicate through the body. This body
channel is used as part of a pairing protocol which allows the devices
to agree on a mutual secret and, at the same time, extract physical
features to verify that they are being held by the same person. We
prove that our device pairing protocol is secure with respect to a
strong threat model and we build a proof of concept set-up and conduct
experiments with 15 people to verify the idea in practice

Bio

Kasper Rasmussen is an Associate Professor in the Computer Science
Department at the University of Oxford. He joined the department in
2013 and in 2015 was awarded a University Research Fellowship from the
Royal Society in London. Prior to being at Oxford, Kasper Rasmussen
spent two years as a post-doc at University of California, Irvine.
Kasper Rasmussen did his Ph.D. with prof. Srdjan Capkun at the
Department of Computer Science at ETH Zurich (Switzerland), where he
worked on security issues relating to secure time synchronization and
secure localization with a particular focus on distance bounding. His
thesis won the “ETH Medal” for an outstanding dissertation from the
Swiss Federal Institute of Technology and he was additionally awarded
the Swiss National Science Foundation (SNSF), Fellowship for
prospective researchers.

Abstract: Underpinned by latest developments in ultra-reliable low-latency 5G, we are able to design a fully immersive next-generation internet. This new internet, the “Internet of Skills”, will allow transmitting physical skills digitally. This talk will explore latest developments in 5G as well as the industry and societal applications thereof.

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