Overall Objectives
Research Program
Application Domains
New Software and Platforms
New Results
Bilateral Contracts and Grants with Industry
Partnerships and Cooperations
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Section: New Results


Digital reputation mechanisms have indeed emerged as a promising approach to cope with the specificities of large scale and dynamic systems. Similarly to real world reputation, a digital reputation mechanism expresses a collective opinion about a target user based on aggregated feedback about his past behavior. The resulting reputation score is usually a mathematical object (e.g. a number or a percentage). It is used to help entities in deciding whether an interaction with a target user should be considered. Digital reputation mechanisms are thus a powerful tool to incite users to behave trustworthily. Indeed, a user who behaves correctly improves his reputation score, encouraging more users to interact with him. In contrast, misbehaving users have lower reputation scores, which makes it harder for them to interact with other users. To be useful, a reputation mechanism must itself be accurate against adversarial behaviors. Indeed, a user may attack the mechanism to increase his own reputation score or to reduce the reputation of a competitor. A user may also free-ride the mechanism and estimate the reputation of other users without providing his own feedback. From what has been said, it should be clear that reputation is beneficial in order to reduce the potential risk of communicating with almost or completely unknown entities. Unfortunately, the user privacy may easily be jeopardized by reputation mechanisms, which is clearly a strong argument to compromise the use of such a mechanism. Indeed, by collecting and aggregating user feedback, or by simply interacting with someone, reputation systems can be easily manipulated in order to deduce user profiles. Thus preserving user privacy while computing robust reputation is a real and important issue that we address in our work [51] . Specifically, our proposal aims at enhancing signatures of reputation mechanism proposed by Bethencourt and his colleagues in 2010 by handling negative votes. Taking into account negative votes implies major modifications with respect to the implementation of the mechanism. Specifically, in the mechanism of Bethencourt and co-authors, service providers locally store votes cast at the end of their interaction with their clients, and compute their reputation score by aggregating the received votes. In particular, they can keep only a subset of them, which clearly makes negative votes useless. We propose to improve upon this solution by guaranteeing that negative votes are taken into account. This is achieved by making both reputation scores and votes of service providers publicly available in order to prevent anyone from modifying or hiding them. Our proposition accomplishes this without jeopardizing the privacy of clients.