Social networks have made a significant impact on how today's Internet users communicate, search for and share data. Numerous proposals have been made to improve existing distributed systems by leveraging the inherent trust built into social links. Many believe that augmenting online marketplaces with social networking should improve trust between transaction partners and improve user satisfaction. In this paper, we perform a detailed study of Overstock Auctions, a novel auction site that integrates social links into user profiles. Using data on connections between roughly 399,000 Overstock users, we evaluate the impact of social connections on business transactions. Our results show that while the majority of users do not engage in social networking, those who transact with friends of friends generally obtain significantly benefits in the form of higher user satisfaction.

Next generation Web 2.0 communities and distributed P2P systems rely on the cooperation of diverse user populations spread across numerous administrative and security domains. Zero accountability via anonymous online identities and divergent interests result in selfish behavior that can disrupt or manipulate networks for personal gain. While "reputation systems" are recognized as a promising means to establish social control for such communities, developing reliable reputation systems remains a challenge. Several unaddressed threats still limit the effectiveness of reputation systems. Furthermore, most existing work on reputations has focused on accurate reputations for stable systems, but not examined the implications of integrating user reputations into scalable distributed infrastructures. The primary goal of this paper is to investigate and address the critical open challenges that limit the effectiveness of reputations. First, we identify a thorough taxonomy on reputation management, and use it as our framework to classify adversarial threats that compromise reliable operation of reputation systems. Second, we survey existing research to address these threats. Finally, we present our solutions to address the two leading reasons for erroneous and misleading values produced by reputation systems today, i.e., user collusion and short-lived online identities. We believe that this paper not only serves as an introduction to reputation systems design, but will also help researchers deploy reliable reputation solutions that contribute towards improving the performance of large distributed applications.

Abstract. Reputation systems help peers decide whom to trust before undertaking a transaction. Conventional approaches to reputation-based trust modeling assume that peers reputed to provide trustworthy service are also likely to provide trustworthy feedback. By basing the credibility of a peer's feedback on its reputation as a transactor, these models become vulnerable to malicious nodes that provide good service to badmouth targeted nodes. We propose to decouple a peer's reputation as a service provider from its reputation as a service recommender, making the reputation more robust to malicious peers. We show via simulations that a decoupled approach greatly enhances the accuracy of reputations generated, resulting in fewer malicious transactions, false positives, and false negatives.

Reputation mechanisms help peers in a peer-to-peer (P2P) system avoid unreliable or malicious peers. In application-level networks, however, short peer life-times mean reputations are often generated from a small number of past transactions. These reputation values are less "reliable," and more vulnerable to bad-mouthing or collusion attacks. We address this issue by introducing proactive reputations, a first-hand history of transactions initiated to augment incomplete or short-term reputation values. We present several mechanisms for generating proactive reputations, along with a statistical similarity metric to measure their effectiveness.

A new generation of distributed systems and applications rely on the cooperation of diverse user populations motivated by self-interest. While they can utilize "reputation systems" to reduce selfish behaviors that disrupt or manipulate the network for personal gain, current reputations face a key challenge in large dynamic networks: vulnerability to peer collusion. In this paper, we propose to dramatically improve the accuracy of reputation systems with the use of a statistical metric that measures the "reliability" of a peer's reputation taking into account collusion-like behavior. Trace-driven simulations on P2P network traffic show that our reliability metric drastically improves system performance. We also apply our metric to 18,000 randomly selected eBay user reputation profiles, and surprisingly discover numerous users with collusion-like behaviors worthy of additional investigation.