Pre-Trusted Peers Probability Influence on Eigen Trust and Reputation Model Over Peer to Peer Distributed Networks

This paper investigates the impact of peer pre-trusted probability on the performance of Eigen's trust and reputation model in distributed wireless networks. Design and develop models for rigorous Eigen Trust assessment and reputation models. In addition, we evaluate our model from performance-based factors namely: accuracy, resource utilization and energy consumption. Finally, the results obtained from our investigation are suggestive of implementation for real-time distributed wireless applications. our proposal

Collusion in Peer-to-Peer Systems

Peer-to-peer systems have reached a widespread use, ranging from academic and industrial applications to home entertainment. The key advantage of this paradigm lies in its scalability and flexibility, consequences of the participants sharing their resources for the common welfare. Security in such systems is a desirable goal. For example, when mission-critical operations or bank transactions are involved, their effectiveness strongly depends on the perception that users have about the system dependability and trustworthiness. A major threat to the security of these systems is the phenomenon of collusion. Peers can be selfish colluders, when they try to fool the system to gain unfair advantages over other peers, or malicious, when their purpose is to subvert the system or disturb other users. The problem, however, has received so far only a marginal attention by the research community. While several solutions exist to counter attacks in peer-to-peer systems, very few of them are meant to directly counter colluders and their attacks. Reputation, micro-payments, and concepts of game theory are currently used as the main means to obtain fairness in the usage of the resources. Our goal is to provide an overview of the topic by examining the key issues involved. We measure the relevance of the problem in the current literature and the effectiveness of existing philosophies against it, to suggest fruitful directions in the further development of the field

Fuzzy-GRA trust model for cloud risk management

Cloud computing is not adequately secure due to the currently used traditional trust methods such as global trust model and local trust model. These are prone to security vulnerabilities. This paper introduces a trust model based on the fuzzy mathematics and gray relational theory. Fuzzy mathematics and gray relational analysis (Fuzzy-GRA) aims to improve the poor dynamic adaptability of cloud computing. Fuzzy-GRA platform is used to test and validate the behavior of the model. Furthermore, our proposed model is compared to other known models. Based on the experimental results, we prove that our model has the edge over other existing models

Tournesol: Permissionless Collaborative Algorithmic Governance with Security Guarantees

Recommendation algorithms play an increasingly central role in our societies. However, thus far, these algorithms are mostly designed and parameterized unilaterally by private groups or governmental authorities. In this paper, we present an end-to-end permissionless collaborative algorithmic governance method with security guarantees. Our proposed method is deployed as part of an open-source content recommendation platform https://tournesol.app, whose recommender is collaboratively parameterized by a community of (non-technical) contributors. This algorithmic governance is achieved through three main steps. First, the platform contains a mechanism to assign voting rights to the contributors. Second, the platform uses a comparison-based model to evaluate the individual preferences of contributors. Third, the platform aggregates the judgements of all contributors into collective scores for content recommendations. We stress that the first and third steps are vulnerable to attacks from malicious contributors. To guarantee the resilience against fake accounts, the first step combines email authentication, a vouching mechanism, a novel variant of the reputation-based EigenTrust algorithm and an adaptive voting rights assignment for alternatives that are scored by too many untrusted accounts. To provide resilience against malicious authenticated contributors, we adapt Mehestan, an algorithm previously proposed for robust sparse voting. We believe that these algorithms provide an appealing foundation for a collaborative, effective, scalable, fair, contributor-friendly, interpretable and secure governance. We conclude by highlighting key challenges to make our solution applicable to larger-scale settings.Comment: 31 pages, 5 figure

Computational Challenges in E-Commerce

Economic and social sciences will drive Internet protocols and services into the future.Engineering and Applied Science

Designing incentives for peer-to-peer systems

Peer-to-peer systems, networks of egalitarian nodes without a central authority, can achieve massive scalability and fault tolerance through the pooling together of individual resources. Unfortunately, most nodes represent self-interested, or rational, parties that will attempt to maximize their consumption of shared resources while minimizing their own contributions. This constitutes a type of attack that can destabilize the system. The first contribution of this thesis is a proposed taxonomy for these rational attacks and the most common solutions used in contemporary designs to thwart them. One approach is to design the P2P system with incentives for cooperation, so that rational nodes voluntarily behave. We broadly classify these incentives as being either genuine or artificial , with the former describing incentives inherent in peer interactions, and the latter describing a secondary enforcement system. We observe that genuine incentives tend to be more robust to rational manipulations than artificial counterparts. Based on this observation, we also propose two extensions to BitTorrent, a P2P file distribution protocol. While this system is popular, accounting for approximately one-third of current Internet traffic, it has known limitations. Our extensions use genuine incentives to address some of these problems. The first extension improves seeding, an altruistic mode wherein nodes that have completed their download continue to provide upload service. We incentivize seeding by giving long-term identifiers to clients enabling seeding clients to be recognized and rewarded in subsequent downloads. Simulations demonstrate that our method is highly effective in protecting swarms from aggressive clients such as BitTyrant. Finally, we introduce The BitTorrent Anonymity Marketplace , wherein each peer simultaneously joins multiple swarms to disguise their true download intentions. Peers then trade one torrent for another, making the cover traffic valuable as a means of obtaining the real target. Thus, when a neighbor receives a request from a peer for blocks of a torrent, it does not know if the peer is really downloading that torrent, or only using it in trade. Using simulation, we demonstrate that nodes cannot determine peer intent from observed interactions

Socially enhanced search and exploration in social tagging networks

Social tagging networks have become highly popular for publishing and searching contents. Users in such networks can review, rate and comment on contents, or annotate them with keywords (emph{social tags}) to give short but exact text representations of even non-textual contents. In addition, there is an inherent support for interactions and relationships among users. Thus, users naturally form groups of friends or of common interests. We address three research areas in our work utilising these intrinsic features of social tagging networks. 1) We investigate new approaches for exploiting the social knowledge of and the relationships between users for searching and recommending relevant contents, and integrate them in a comprehensive framework, coined SENSE, for search in social tagging networks. 2) To dynamically update precomputed lists of transitive friends in descending order of their distance in user graphs of social tagging networks, we provide an algorithm for incrementally solving the all pairs shortest distance problem in large, disk-resident graphs and formally prove its correctness. 3) Since users are content providers in social tagging networks, users may keep their own data at independent, local peers that collaborate in a distributed P2P network. We provide an algorithm for such systems to counter cheating of peers in authority computations over social networks. The viability of each solution is demonstrated by extensive experiments regarding effectiveness and efficiency.Im Internet sind soziale Netzwerke, die es erlauben Inhalte mit Anmerkungen zu versehen, inzwischen weit verbreitet und bei Anwendern gleichermaßen beliebt, um eigene Informationen zu veröffentlichen oder nach denen andere Benutzer zu suchen. Anwender können in diesen sozialen Netzwerken vorhandene Inhalte kritisieren, bewerten und kommentieren oder eben mit Schlagworten, d.h. mit sozialen Annotationen (engl. social tags) versehen. Ein weiteres Merkmal dieser sozialen Netzwerke ist es, dass Interaktionen und Freundshaftsbeziehungen zwischen Benutzern aktiv gefördert werden und sich so Anwender mit ähnlichen Interessen in Gruppen zusammenschließen. Hieraus ergeben sich interessante Möglichkeiten für die Forschung. Wir sprechen drei Bereiche in dieser Arbeit an. 1) Wir präsentieren mit SENSE ein umfassendes Rahmenwerk zur Suche in sozialen Netzwerken und stellen darin neue Ansätze zur Verbesserung von Suchergebnissen vor, die das gemeinschaftliche Wissen der Anwender und die Beziehungen zwischen den Anwendern nutzen. 2) Zur kontinuierlichen Aktualisierung von Freundeslisten, stellen wir einen Algorithmus zur inkrementellen Lösung des kürzesten Wege-Problems zwischen allen Paaren von Knoten im Benutzergraphen sozialer Netzwerke vor. 3) Soziale Netzwerke, die in einer verteilten P2P Umgebung betrieben werden, stehen dem Problem gegenüber, dass Benutzer-Peers versuchen können, Suchergebnisse zu beeinflussen. Wir stellen einen Algorithmus vor, der diesem Problem entgegentritt

Strategische Analyse von Anreizmechanismen in strukturierten Peer-to-Peer Systemen

In strukturierten P2P Systemen können Teilnehmer ihren Nutzen erhöhen indem sie keine Anfragen bearbeiten. Dieses Problem wird mit experimenteller Wirtschaftsforschung untersucht. Menschliche Teilnehmer übernehmen dabei die Rolle eines Peers. Sie spielen Schwellwertstrategien, verzichten auf Feedback und ändern ihre Strategien nicht, wenn Andere mehr verdienen als sie selbst. Formal und mit Hilfe von Simulationen wird gezeigt, dass diese Strategien zu einem effizienten Gleichgewicht führen