From Hazardous Behaviours to a Risk Metric for Reputation Systems in Peer to Peer Networks

International audiencePeer to Peer systems have shown to be very powerful to build very large scale distributed information systems. They are self organized, and provide very high availability of the data. However, the management of malicious peers is a very open problem for the Peer to Peer research community, and building trust is a very difficult task. In this context, Reputation Systems have shown to be a very good solution to build trust in Peer to Peer systems. Nevertheless, using only the reputation value of a peer to decide to make a transaction is not sufficient to guarantee that it will succeed, and the use of the credibility of recommendation emitters does not always significantly mitigate the computed reputation. We show in this paper the importance of the notion of risk associated to the reputation value, and why a better decision can be taken using both, the reputation and a risk value, for a given peer. We present some metrics based on the list of recommendations for a peer that allow to detect some suspicious behaviours that can alert the application of the presence of a malicious peer. The proposed metric is flexible such that an application can adapt the metric to its needs, given more or less weight to some specific types of behaviours. We present some simulations to show the influence of malicious behaviours of a peer over its reputation value with the evaluation of the associated risk, and how our metric can detect this kind of behaviours. We conclude about the need to use a risk factor associated to the reputation value, and present some future works about the risk metrics

Choosing reputable resources in unstructured peer-to-peer networks using trust overlays

In recent years Peer-to-Peer Systems have gained popularity, and are best known as a convenient way of sharing content. However, even though they have existed for a considerable length of time, no method has yet been developed to measure the quality of the service they provide nor to identify cases of misbehaviour by individual peers. This thesis attempts to give to P2P systems some quality measures with the potential of giving querying peers criteria by which to judge and make predictions about the behaviour of their counterparts. The work includes the design of a reputation system from which querying peers can seek guidance before they commit to transaction with another peer. but usually as Reputation and Recommender systems have existed for years centralized services. Our innovation is the use of a distributed recommendation system which will be supported by the peers themselves. The system operates in the same manner as "word-of-mouth" in human societies does. In contrast to other reputation systems the word-of-mouth technique is itself decentralized since there is no need for central entities to exist as long as there are participants willing to be involved in the recommendation process. In order for a society to exist it is necessary that members have some way of knowing each other so that they can form relationships. The main element used to link members in an online community together is a virtual trust relationship that can be identified from the evidence that exists about their virtual partnerships. In our work we approximate the level of trust that could exist between any two parties by exploiting their similarity, constructing a network that is known as "web of trust". Using the transitivity property of trust, we make it possible for more peers to come in to contact through virtual trust relationships and thus get better results than in an ordinary system.EThOS - Electronic Theses Online ServiceGreek State Scholarships FoundationGBUnited Kingdo

Data centric trust evaluation and prediction framework for IOT

© 2017 ITU. Application of trust principals in internet of things (IoT) has allowed to provide more trustworthy services among the corresponding stakeholders. The most common method of assessing trust in IoT applications is to estimate trust level of the end entities (entity-centric) relative to the trustor. In these systems, trust level of the data is assumed to be the same as the trust level of the data source. However, most of the IoT based systems are data centric and operate in dynamic environments, which need immediate actions without waiting for a trust report from end entities. We address this challenge by extending our previous proposals on trust establishment for entities based on their reputation, experience and knowledge, to trust estimation of data items [1-3]. First, we present a hybrid trust framework for evaluating both data trust and entity trust, which will be enhanced as a standardization for future data driven society. The modules including data trust metric extraction, data trust aggregation, evaluation and prediction are elaborated inside the proposed framework. Finally, a possible design model is described to implement the proposed ideas