A trust-based probabilistic recommendation model for social networks

Zhang

International Journal of Distributed Sensor Networks

et al. 2016

Establishing trust and reputation for evaluation of message reliability is key to the vehicular ad hoc networks (VANETs). Most of the previous reputation management systems focus on the effectiveness of the reputation management system in handling the liars who send false service messages. However, these reputation management systems have two drawbacks. One is that they are vulnerable to tactical attacks such as self-promoting attacks and bad-mouthing attacks. The other is that they may violate location privacy because they assume every vehicle communicates with a unique ID. Our research particularly investigates the robustness against these tactical attacks, as well as the preservation of privacy by integrating trust management with the pseudonym technique. To resist the tactical attacks in VANETs, we present a reputation model which builds both service reputation and feedback reputation. Moreover, we apply the information entropy and the majority rule to the reputation accumulation algorithms to counter false feedback. To defend the reputation link attack during pseudonym changes, we propose hidden-zone strategy and -anonymity strategy. The simulation results show that our scheme is robust to these tactical attacks and preserves privacy against the reputation link attack during the pseudonym changes.

Section: Experience-based Trust and Role-based Trustmentioning

confidence: 99%

RPRep: A Robust and Privacy-Preserving Reputation Management Scheme for Pseudonym-Enabled VANETs

Zhang

International Journal of Distributed Sensor Networks

et al. 2016

“…Similarly, in a cross-domain scenario, the tastes of two users belonging to two different domains are similar if they have trust relations. Therefore, we can easily apply traditional trustaware recommendation method to cross-domain scenario using equation (1). In cross-domain scenarios, trust connections can exist with some different forms.…”

Section: B Coarse Rating Predictionmentioning

confidence: 99%

“…These systems recommend online products, music, and movies to users. In the past decade, lots of researchers have worked to develop recommender systems [1], [2], but there are still some challenges like cold start [3], [4], [5] and data sparsity [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Exploiting Trust and Usage Context for Cross-Domain Recommendation

Zhang

et al. 2016

IEEE Access

Cross-domain recommender systems are usually able to suggest items which are not in the same domain where users provided ratings. For this reason, cross-domain recommendation has attracted more and more attention in recent years. However, most studies propose to make cross-domain recommendation in the scenario where there are common ratings between different domains. The scenario without common ratings is seldom considered. In this paper, we propose a novel method to solve the cross-domain recommendation problem in such a scenario. We first apply trust relations to the cross-domain scenario for predicting coarse ratings pertaining to cross-domain items. Then we build a new rating matrix including known ratings and predicted ratings of items from different domains, and transform a user-item matrix into an item-item association matrix. Finally, we compute the similarities of items belonging to different domains and use item-based collaborative filtering to generate recommendations. Through relevant experiments on a real-world dataset, we compare our method to a trust-aware recommendation method and demonstrate its effectiveness in terms of prediction accuracy, recall, and coverage.

“…In addition to bio-inspired trust model, the trust also modeled using other existing algorithms on internets such as probability theory or game theory. Research utilizing probability theory for building the trust model, among others, performed by [16][17], while research on game theory-based was developed by [18].…”

Section: Introductionmentioning

confidence: 99%

ConTrust: A Trust Model to Enhance the Privacy in Internet of Things

Suryani¹,

Sulistyo²,

Widyawan³

2017

IJIES

Abstract:The objects connected to the Internet of Things require security. Security can do a variety of forms, ranging from data encryption to trust management mechanism. Privacy aspects can be improved through the process of trust assessment. Here privacy is improved using Diffie-Hellman key distribution and trust assessment to ensure only trusted things can communicate in the IoT environment. This paper proposed new trust assessment model namely ConTrust. Inspired by the experience of everyday life, ConTrust uses two parameters for assessing trust value: current trust assessment and history-based reputation. History parameter is used in the formula of trust assessment in order to make a fair calculation based on its past object experiences. The formula also utilizes time parameter to improve the fluctuating values of reputation. Some simulations were conducted to evaluate ConTrust and showed that the time parameter is very important factors that influenced the stability of trust and reputation value.