Communications of the ACM
On power-law relationships of the Internet topology
Proceedings of the conference on Applications, technologies, architectures, and protocols for computer communication
IPTPS '01 Revised Papers from the First International Workshop on Peer-to-Peer Systems
Shilling recommender systems for fun and profit
Proceedings of the 13th international conference on World Wide Web
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
SybilGuard: defending against sybil attacks via social networks
Proceedings of the 2006 conference on Applications, technologies, architectures, and protocols for computer communications
Securing decentralized reputation management using TrustGuard
Journal of Parallel and Distributed Computing - Special issue: Security in grid and distributed systems
Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness
ACM Transactions on Internet Technology (TOIT)
Matchbox: large scale online bayesian recommendations
Proceedings of the 18th international conference on World wide web
Sybil-resilient online content voting
NSDI'09 Proceedings of the 6th USENIX symposium on Networked systems design and implementation
The wisdom of the few: a collaborative filtering approach based on expert opinions from the web
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Personalised and dynamic trust in social networks
Proceedings of the third ACM conference on Recommender systems
The SocialTrust framework for trusted social information management: Architecture and algorithms
Information Sciences: an International Journal
Dishonest behaviors in online rating systems: cyber competition, attack models, and attack generator
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
Using probabilistic confidence models for trust inference in Web-based social networks
ACM Transactions on Internet Technology (TOIT)
Voting Systems with Trust Mechanisms in Cyberspace: Vulnerabilities and Defenses
IEEE Transactions on Knowledge and Data Engineering
CoBayes: bayesian knowledge corroboration with assessors of unknown areas of expertise
Proceedings of the fourth ACM international conference on Web search and data mining
Comparing context-aware recommender systems in terms of accuracy and diversity
User Modeling and User-Adapted Interaction
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Social trust and recommendation services are the most popular social rating systems today for service providers to learn about the social opinion or popularity of a product, item, or service, such as a book on Amazon, a seller on eBay, a story on Digg or a movie on Netflix. Such social rating systems are very convenient and offer alternative learning environments for decision makers, but they open the door for attackers to manipulate the social rating systems by selfishly promoting or maliciously demoting certain items. Although a fair amount of effort has been made to understand various risks and possible defense mechanisms to counter such attacks, most of the existing work to date has been devoted to studying specific types of attacks and their countermeasures. In this article, we argue that vulnerabilities in social rating systems and their countermeasures should be examined and analyzed in a systematic manner. We first give an overview of the common vulnerabilities and attacks observed in some popular social rating services. Next, we describe three types of attack strategies in two types of social rating systems, including a comprehensive theoretical analysis of their attack effectiveness and attack costs. Three context-aware countermeasures are then presented: (i) hiding user-item relationships, (ii) using confidence weight to distinguish popular and unpopular items, and (iii) incorporating time windows in trust establishment. We also provide an in-depth discussion on how these countermeasures can be used effectively to improve the robustness and trustworthiness of the social rating services.