A Privacy Preserving Distributed Reputation Mechanism

International audienceReputation systems allow to estimate the trustworthiness of entities based on their past behavior. Electronic commerce, peer-to-peer routing and collaborative environments, just to cite a few, highly benefit from using reputation systems. To guarantee an accurate estimation, reputation systems typically rely on a central authority, on the identification and authentication of all the participants, or both. In this paper, we go a step further by presenting a distributed reputation mechanism which is robust against malicious behaviors and that preserves the privacy of its clients. Guaranteed error bounds on the estimation are provided

A Privacy Preserving Distributed Reputation Mechanism

International audienceReputation systems allow to estimate the trustworthiness of entities based on their past behavior. Electronic commerce, peer-to-peer routing and collaborative environments, just to cite a few, highly benefit from using reputation systems. To guarantee an accurate estimation, reputation systems typically rely on a central authority, on the identification and authentication of all the participants, or both. In this paper, we go a step further by presenting a distributed reputation mechanism which is robust against malicious behaviors and that preserves the privacy of its clients. Guaranteed error bounds on the estimation are provided

Fuzzy Privacy Preserving Peer-to-Peer Reputation Management

The P2PRep algorithm is a reputation-management mechanism in which a peer uses fuzzy techniques to compute local reputations and aggregates these results to compute a global reputation for another peer which has made an offer of service. While this mechanism is known to be extremely effective in the presence of malicious peers, it has one drawback: it does not preserve the anonymity of peers in the network during the voting phase of protocol. This makes it unsuitable for use in networks which associate peers with a routing identifier such as an IP address. We propose in this paper, a solution to this problem - the 3PRep (Privacy Preserving P2PRep) algorithm which implements two protocols to maintain vote privacy in P2PRep without significant additional computation and communications overhead. In doing so, we also provide a method to compute the Ordered Weighted Average (OWA) over distributed datasets while maintaining privacy of these data

Réputation et respect de la vie privée dans les réseaux dynamiques auto-organisés

Reputation mechanisms are very powerful mechanisms to foster trust between unknown users, by rewarding good behaviors and punishing bad ones. Reputation mechanisms must guarantee that the computed reputation scores are precise and robust against attacks; to guarantee such properties, existing mechanisms require information that jeopardize users' privacy: for instance, clients' interactions might be tracked. Privacy-preserving reputation mechanisms have thus been proposed, protecting both clients' privacy and the providers' one. However, to guarantee strong privacy properties, these mechanisms provide imprecise reputation scores, particularly by preventing clients to testify about their negative interactions. In this thesis, we propose a new distributed privacy-preserving reputation mechanism allowing clients to issue positive as well as negative feedback. Such a construction is made possible thanks to tools from the distributed systems community -- distributed third parties that allow for a distribution of trust and that tolerate malicious behaviors -- as well as from the cryptographic one -- for instance zero-knowledge proofs of knowledge or anonymous proxy signatures. Furthermore, we prove that our mechanism guarantees the required privacy and security properties, and we show with theoretical and practical analysis that this mechanism is usable.Les mécanismes de réputation sont des outils très utiles pour inciter des utilisateurs ne se connaissant pas à se faire confiance, en récompensant les bons comportements et, inversement, en pénalisant les mauvais. Cependant, pour que la réputation des fournisseurs de service soit précise et robuste aux attaques, les mécanismes de réputation existants requièrent de nombreuses informations qui menacent la vie privée des utilisateurs; par exemple, il est parfois possible de traquer les interactions effectuées par les clients. Des mécanismes de réputation préservant aussi bien la vie privée des clients que celle des fournisseurs sont donc apparus pour empêcher de telles attaques. Néanmoins, pour garantir des propriétés fortes de vie privée, ces mécanismes ont dû proposer des scores de réputation imprécis, notamment en ne permettant pas aux clients de témoigner de leurs interactions négatives.Dans cette thèse, nous proposons un nouveau mécanisme de réputation distribué préservant la vie privée, tout en permettant aux clients d'émettre des témoignages négatifs. Une telle construction est possible grâce à des outils issus des systèmes distribués -- des tierces parties distribuées qui permettent de distribuer la confiance et de tolérer des comportements malveillants -- et de la cryptographie -- par exemple des preuves de connaissance à divulgation nulle de connaissance ou des signatures proxy anonymes. Nous prouvons de plus que ce mécanisme garantit les propriétés de vie privée et de sécurité nécessaires, et montrons par des analyses théoriques et pratiques que ce mécanisme est utilisable

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems

Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges

open access articleThis paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture