Esma Aïmeur

A lambic : a privacy-preserving recommender system for electronic commerce

Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. Customers should be able to keep private their personal information, including their buying preferences, and they should not be tracked against their will. The commercial interests of merchants should also be protected by allowing them to make accurate recommendations without revealing legitimately compiled valuable information to third parties. We introduce a theoretical approach for a system called Alambic, which achieves the above privacy-protection objectives in a hybrid recommender system that combines content-based, demographic and collaborative filtering techniques. Our system splits customer data between the merchant and a semi-trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system could only be subverted by a coalition between these two parties.

Analyzing a new learning strategy according to different knowledge levels

Abstract Intelligent tutoring systems have recently evolved towards a co-operative approach between the learner and the system. Knowledge acquisition is facilitated by interaction with the system under the control of the learner. New tutoring strategies have been introduced to enhance motivation of the learner by involving a second learner or a companion who simulates the behaviour of a second learner in the learning process. An inverted model called “learning by teaching” in which the learner could teach the learning companion by giving explanations has also been presented. In this paper we discuss the advantage and the inconvenience of these strategies and present a new learning strategy which improves performance for good or intermediate learners. We describe an experiment with this strategy and compare results with those obtained with the companion. We analyze and discuss results obtained.

Privacy protection issues in social networking sites

Social Networking Sites (SNS) have become very popular during the past few years, as they allow users to both express their individuality and meet people with similar interests. Nonetheless, there are also many potential threats to privacy associated with these SNS such as identity theft and disclosure of sensitive information. However, many users still are not aware of these threats and the privacy settings provided by SNS are not flexible enough to protect user data. In addition, users do not have any control over what others reveal about them. As such, we conduct a preliminary study which examines the privacy protection issues on Social Networking Sites (SNS) such as MySpace, Facebook and LinkedIn. Based on this study, we identify three privacy problems in SNS and propose a Privacy Framework as a foundation to cope with these problems.

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.

An Actor Based Architecture for Intelligent Tutoring Systems

The evolution of intelligent tutoring systems (ITS) toward the use of multiple learning strategies calls on a multi-agent architecture. We designed an ITS where several agents assume different pedagogical roles; consequently, we called them actors. We first describe the conceptual architecture of an actor which allows it to be reactive, instructable, adaptive and cognitive. We then provide a detailed view of this architecture and show how it functions with an example involving the different actors of a new learning strategy, the learning by disturbing strategy.

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.

Experimental Demonstration of a Hybrid Privacy-Preserving Recommender System

Recommender systems enable merchants to assist customers in finding products that best satisfy their needs. Unfortunately, current recommender systems suffer from various privacy-protection vulnerabilities. We report on the first experimental realization of a theoretical framework called ALAMBIC, which we had previously put forth to protect the privacy of customers and the commercial interests of merchants. Our system is a hybrid recommender that combines content-based, demographic and collaborative filtering techniques. The originality of our approach is to split customer data between the merchant and a semi- trusted third party, so that neither can derive sensitive information from their share alone. Therefore, the system can only be subverted by a coalition between these two parties. Experimental results confirm that the performance and user-friendliness of the application need not suffer from the adoption of such privacy-protection solutions. Furthermore, user testing of our prototype show that users react positively to the privacy model proposed.

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.

LANCA: A Distance Learning Architecture Based on Networked Cognitive Agents

The use of Internet as a general vehicle to support distance learning is a recent orientation of learning with multiple positive and negative consequences. The important disadvantage of such an approach is to forget the difference between information and knowledge, between consultation and pedagogy, leading to poor training as a consequence. The need of providing access to information to a larger number of people should not be realized to the detriment of the quality of training. In this paper we first explain why and how ITS techniques using intelligent agents can be adapted to distance learning. We precise the main characteristics of these agents and their functions in a distributed environment. We then present the architecture of this environment with the role of the different intelligent agents. We show, on an example, how the agents interact with the learner and particularly how a pedagogical agent can switch to a new strategy according to the progression of the learner.