The Crypto-democracy and the Trustworthy

In the current architecture of the Internet, there is a strong asymmetry in terms of power between the entities that gather and process personal data (e.g., major Internet companies, telecom operators, cloud providers, ...) and the individuals from which this personal data is issued. In particular, individuals have no choice but to blindly trust that these entities will respect their privacy and protect their personal data. In this position paper, we address this issue by proposing an utopian crypto-democracy model based on existing scientific achievements from the field of cryptography. More precisely, our main objective is to show that cryptographic primitives, including in particular secure multiparty computation, offer a practical solution to protect privacy while minimizing the trust assumptions. In the crypto-democracy envisioned, individuals do not have to trust a single physical entity with their personal data but rather their data is distributed among several institutions. Together these institutions form a virtual entity called the Trustworthy that is responsible for the storage of this data but which can also compute on it (provided first that all the institutions agree on this). Finally, we also propose a realistic proof-of-concept of the Trustworthy, in which the roles of institutions are played by universities. This proof-of-concept would have an important impact in demonstrating the possibilities offered by the crypto-democracy paradigm.Comment: DPM 201

Uniform and Ergodic Sampling in Unstructured Peer-to-Peer Systems with Malicious Nodes

ISBN: 978-3-642-17652-4International audienceWe consider the problem of uniform sampling in large scale open systems. Uniform sampling is a fundamental schema that guarantees that any individual in a population has the same probability to be selected as sample. An important issue that seriously hampers the feasibility of uniform sampling in open and large scale systems is the inevitable presence of malicious nodes. In this paper we show that restricting the number of requests that malicious nodes can issue and allowing for a full knowledge of the composition of the system is a necessary and sufficient condition to guarantee uniform and ergodic sampling. In a nutshell, a uniform and ergodic sampling guarantees that any node in the system is equally likely to appear as a sample at any non malicious node in the system and that infinitely often any nodes have a non null probability to appear as a sample at any honest nodes

Scalable and Secure Aggregation in Distributed Networks

We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability

Scalable and Secure Aggregation in Distributed Networks

We consider the problem of computing an aggregation function in a \emph{secure} and \emph{scalable} way. Whereas previous distributed solutions with similar security guarantees have a communication cost of $O(n^3)$, we present a distributed protocol that requires only a communication complexity of $O(n\log^3 n)$, which we prove is near-optimal. Our protocol ensures perfect security against a computationally-bounded adversary, tolerates $(1/2-\epsilon)n$ malicious nodes for any constant $1/2 > \epsilon > 0$ (not depending on $n$), and outputs the exact value of the aggregated function with high probability

Privacy-Preserving Boosting

International audienc

An optimal quantum algorithm to approximate the mean and its application for approximating the median of a set of points over an arbitrary distance

Ten pages, no figures, three algorithmsWe describe two quantum algorithms to approximate the mean value of a black-box function. The first algorithm is novel and asymptotically optimal while the second is a variation on an earlier algorithm due to Aharonov. Both algorithms have their own strengths and caveats and may be relevant in different contexts. We then propose a new algorithm for approximating the median of a set of points over an arbitrary distance function

Concilier l'équité statistique et la précision en apprentissage machine interprétable grâce à la PLNE

International audienceL'interprétabilité et l'équité sont deux propriétés de plus en plus recherchées en apprentissage machine. Toutefois, l'apprentissage de modèles interprétables optimaux et respectant des contraintes d'équité statistique a été identifié comme l'un des grands défis techniques à l'interprétabilité. En effet, les contraintes d'équité modifient l'espace des solutions admissibles, rendant plus difficile son exploration.FairCORELS est un algorithme d'apprentissage supervisé permettant l'apprentissage de modèles de type listes de règles certifiés optimaux et respectant des contraintes d'équité. FairCORELS est basé sur CORELS, un algorithme de branch-and-bound permettant l'apprentissage de listes de règles optimales. En raison des contraintes d'équité imposées dans FairCORELS, certaines structures de données utilisées dans CORELS ne peuvent plus être utilisées, et l'exploration de l'espace de recherche est alors plus difficile.Nous proposons une approche basée sur la PLNE tirant profit des contraintes d'équité, et de leur interaction avec la précision, pour élaguer l'espace de recherche efficacement et guider son exploration. Les expérimentations menées montrent que l'approche proposée accélère la convergence et permet l'apprentissage de modèles équitables optimaux

Concilier l'équité statistique et la précision en apprentissage machine interprétable grâce à la PLNE

International audienceL'interprétabilité et l'équité sont deux propriétés de plus en plus recherchées en apprentissage machine. Toutefois, l'apprentissage de modèles interprétables optimaux et respectant des contraintes d'équité statistique a été identifié comme l'un des grands défis techniques à l'interprétabilité. En effet, les contraintes d'équité modifient l'espace des solutions admissibles, rendant plus difficile son exploration.FairCORELS est un algorithme d'apprentissage supervisé permettant l'apprentissage de modèles de type listes de règles certifiés optimaux et respectant des contraintes d'équité. FairCORELS est basé sur CORELS, un algorithme de branch-and-bound permettant l'apprentissage de listes de règles optimales. En raison des contraintes d'équité imposées dans FairCORELS, certaines structures de données utilisées dans CORELS ne peuvent plus être utilisées, et l'exploration de l'espace de recherche est alors plus difficile.Nous proposons une approche basée sur la PLNE tirant profit des contraintes d'équité, et de leur interaction avec la précision, pour élaguer l'espace de recherche efficacement et guider son exploration. Les expérimentations menées montrent que l'approche proposée accélère la convergence et permet l'apprentissage de modèles équitables optimaux