Sébastien Gambs

Towards a Privacy-Enhanced Social Networking Site

Social Networking Sites (SNS), such as Facebook and LinkedIn, have become the established place for keeping contact with old friends and meeting new acquaintances. As a result, a user leaves a big trail of personal information about him and his friends on the SNS, sometimes even without being aware of it. This information can lead to privacy drifts such as damaging his reputation and credibility, security risks (for instance identity theft) and profiling risks. In this paper, we first highlight some privacy issues raised by the growing development of SNS and identify clearly three privacy risks. While it may seem a priori that privacy and SNS are two antagonist concepts, we also identified some privacy criteria that SNS could fulfill in order to be more respectful of the privacy of their users. Finally, we introduce the concept of a Privacy-enhanced Social Networking Site (PSNS) and we describe Privacy Watch, our first implementation of a PSNS.

Machine learning in a quantum world

Quantum Information Processing (QIP) performs wonders in a world that obeys the laws of quantum mechanics, whereas Machine Learning (ML) is generally assumed to be done in a classical world. We initiate an investigation of the encounter of ML with QIP by defining and studying novel learning tasks that correspond to Machine Learning in a world in which the information is fundamentally quantum mechanical. We shall see that this paradigm shift has a profound impact on the learning process and that our classical intuition is often challenged.

Privacy-preserving boosting

We describe two algorithms, BiBoost (Bipartite Boosting) and MultBoost (Multiparty Boosting), that allow two or more participants to construct a boosting classifier without explicitly sharing their data sets. We analyze both the computational and the security aspects of the algorithms. The algorithms inherit the excellent generalization performance of AdaBoost. Experiments indicate that the algorithms are better than AdaBoost executed separately by the participants, and that, independently of the number of participants, they perform close to AdaBoost executed using the entire data set.

UPP: User Privacy Policy for Social Networking Sites

Since their introduction, SNS (Social Networking Sites) such as MySpace, Facebook and LinkedIn have attracted millions of users and have become established places for keeping contact with old acquaintances and meeting new ones. Nonetheless, due to lack of user awareness and proper privacy protection tools, huge quantities of user data, including personal information, pictures and videos are quickly falling into the hands of authorities, strangers, recruiters and even the public at large. By using SNSs and accepting their privacy policy, users have volunteered to relinquish their ownership on their own data, which explains why the proposed privacy solutions based on current SNSs cannot solve all user privacy issues. As such, we start by setting the foundations for privacy and introduce a Privacy Framework for SNSs. Then, based on this framework, we present a User Privacy Policy (UPP) which provides users with an easy and flexible way to specify and communicate their privacy concerns to other users, third parties and to the SNS provider.

Quantum clustering algorithms

By the term quantization, we refer to the process of using quantum mechanics in order to improve a classical algorithm, usually by making it go faster. In this paper, we initiate the idea of quantizing clustering algorithms by using variations on a celebrated quantum algorithm due to Grover. After having introduced this novel approach to unsupervised learning, we illustrate it with a quantized version of three standard algorithms: divisive clustering, k-medians and an algorithm for the construction of a neighbourhood graph. We obtain a significant speedup compared to the classical approach.

Anonymous Quantum Communication

We introduce the first protocol for the anonymous transmission of a quantum state that is information-theoretically secure against an active adversary, without any assumption on the number of corrupt participants. The anonymity of the sender and receiver is perfectly preserved, and the privacy of the quantum state is protected except with exponentially small probability. Even though a single corrupt participant can cause the protocol to abort, the quantum state can only be destroyed with exponentially small probability: if the protocol succeeds, the state is transferred to the receiver and otherwise it remains in the hands of the sender (provided the receiver is honest).

CLARISSE: A Machine Learning Tool to Initialize Student Models

The initialization of the student model in an intelligent tutoring system is a crucial issue. It is not realistic to assume that each new student has the same prior knowledge concerning the topic being taught, be it nothing or some standard prior knowledge. We introduce CLARISSE, which is a novel categorization method. We illustrate this tool with the identification of categories among students for QUANTI, an intelligent tutoring system for the teaching of quantum information processing. In order to classify a new learner, CLARISSE generates an adaptive pre-test that can identify with high accuracy the learners category after very few questions.

Anonymous quantum communication

We present the first protocol for the anonymous transmission of a quantum state that is information-theoretically secure against an active adversary, without any assumption on the number of corrupt participants. The anonymity of the sender and receiver, as well as the privacy of the quantum state, are perfectly protected except with exponentially small probability. Even though a single corrupt participant can cause the protocol to abort, the quantum state can only be destroyed with exponentially small probability: if the protocol succeeds, the state is transferred to the receiver and otherwise it remains in the hands of the sender (provided the receiver is honest).

An Empirical Study on GSN Usage Intention: Factors Influencing the Adoption of Geo-Social Networks

Nowadays, geosocial networks (GSNs) have become a significant component of peoples daily lives as they are one of the most popular applications that are being widely accessed through smart devices such as smartphones and tablets. Their rapid widespread use and their invasion of our private life warrant a better understanding. In particular, the impact of trust in GSN, the privacy concerns of users, their perception of risk and the social influence on the use of such mobile applications is not yet fully understood. In this paper, we study the factors influencing the usage intention of GSN users. To realize this, we propose a model based on the users perspective. Our model focuses on four overall factors that influence the users concerns and in turn their intention and aim of using GSNs: privacy concerns, trust, social influence and risk perception. We tested empirically the proposed research model by running a web-based survey. The participants consisted of 396 persons with at least a past experience with GSNs. The results revealed that among all the possible factors the privacy concerns, social influence and trust have a significant impact on the intention and usage of GSNs. In contrast, personality traits have almost no effects on trust or social influence. One notable exception is computer self-efficacy that was found to induce a strong influence on the four principal factors.