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
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.
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.
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.
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
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
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
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.
Complex Event Processing over Data Streams
The concept of event processing is established as a generic computational paradigm in various application fields, ranging from data processing in Web environments, over maritime and transport, to finance and medicine. Events report on state changes of a system and its environment. Complex Event Processing (CEP) in turn, refers to the identification of complex/composite events of interest, which are collections of simple events that satisfy some pattern, thereby providing the opportunity for reactive and proactive measures. Examples include the recognition of attacks in computer network nodes, human activities on video content, emerging stories and trends on the Social Web, traffic and transport incidents in smart cities, fraud in electronic marketplaces, etc. The goal of this talk is to first provide an overview of this field and second discuss some major challenges that arise due to the high volume and velocity of the generated event streams. In the end I will discuss the building blocks of our recent system to mitigate the inherent issues in CEP.
Syed Gillani is currently an ATER in CITI INSA Lyon. His research interests are in the broad area of database systems, stream processing, query optimisations and Semantic Web. During his PhD he proposed various techniques to bridge the gap between core Semantic Web concepts and database optimisation techniques. Furthermore, he proposed a new query language and its implementation for the Semantic Complex Event Processing.
Simulation multi-agents et calcul haute performance sur carte
Nombre de systèmes complexes sont aujourd’hui étudiés par simulation
grâce à des modèles basés sur le paradigme multi-agents. Dans ces
modèles, les individus, leur environnement et leurs interactions sont
directement représentés. Ce type de simulation nécessite parfois de
considérer un grand nombre d’entités, ce qui pose des problèmes de
performance et de passage à l’échelle. Dans ce cadre, la programmation
sur carte graphique (GPGPU) est une solution attrayante : elle permet
des gains de performances très conséquents sur des ordinateurs
personnels. Le GPGPU nécessite cependant une programmation extrêmement
spécifique qui limite à la fois son accessibilité et la réutilisation
des développements réalisés, ce qui est particulièrement vrai dans le
contexte de la simulation multi-agents. Dans cet exposé, nous
présenterons cette technologie et les travaux de recherche que nous
avons réalisés afin de pallier ces difficultés. Nous décrirons en
particulier une méthode de conception, appelée délégation GPU, qui
permet (1) d’adapter les modèles multi-agents au contexte du GPGPU et de
(2) faciliter la réutilisation des développements associés.
Fabien Michel est titulaire d’un doctorat en informatique obtenu à
l’Université de Montpellier en 2004. De 2005 à 2008, il a exercé en tant
que maître de conférences au CReSTIC de Reims avant de rejoindre le
Laboratoire d’Informatique, de Robotique et de Microélectronique de
Montpellier (LIRMM) où il exerce actuellement. Ses recherches
s’inscrivent principalement dans le domaine de la modélisation et de la
simulation de systèmes multi-agents (SMA) et reposent sur la proposition
de modèles formels et conceptuels (e.g. le modèle IRM4S) et d’outils
logiciels génériques (plates-formes MaDKit et TurtleKit), ainsi que sur
leur utilisation dans divers domaines tels que le jeu vidéo, le
traitement numérique de l’image ou la robotique collective. Plus
spécifiquement, le fil rouge de ses travaux, synthétisé dans son HDR
obtenue en 2015, repose sur une approche dite
« environement-centrée » (E4MAS) : contrairement aux approches centrées
sur la conception des comportements individuels, il s’agit de considérer
l’environnement des agents comme une abstraction de premier ordre dont
le rôle est primodial. En particulier, il a récemment décliné cette
démarche afin de proposer une approche originale dans le cadre de
l’utilisation du calcul haute performance sur carte graphique (GPGPU)
pour la simulation de SMA.