Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior

Dellarocas, Chrysanthos

doi:10.1145/352871.352889

Cited by 473 publications

(318 citation statements)

References 5 publications

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“…For example, community members can build a good reputation, milk it by cheating other members and then disappear and reappear under a new online identity and a clean record (Friedman and Resnick, 2001). They can use fake online identities to post dishonest feedback and thus try to inflate their reputation or tarnish that of their competitors (Dellarocas, 2000). Finally, the mediated nature of online feedback mechanisms raises questions related to the trustworthiness of their operators.…”

Section: Scale Enables New Applicationsmentioning

confidence: 99%

“…The development of effective mechanisms for dealing with collusive efforts to manipulate online ratings is currently an active area of research. Dellarocas (2000;2001a) explores the use of robust statistics in aggregating individual ratings as a mechanism for reducing the effects of coordinated efforts to bias ratings. To this date, however, there is no effective solution that completely eliminates the problem.…”

Section: Eliciting Sufficient and Honest Feedbackmentioning

confidence: 99%

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The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

2003

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1 :Online feedback mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. Their growing popularity has potentially important implications for a wide range of management activities, such as brand building, customer acquisition and retention, product development, and quality assurance. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based feedback mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, management science, sociology, and psychology in order to take into consideration the special properties of online environments. Finally, it identifies opportunities that this new area presents for OR/MS research.

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Section: Scale Enables New Applicationsmentioning

confidence: 99%

Section: Eliciting Sufficient and Honest Feedbackmentioning

confidence: 99%

The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

2003

Self Cite

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“…Collusion occurs when two of more peers collectively boost one another's reputations or conspire against one or more peers in the network. Dellarocas identifies four types of collusion misbehavior [12]. In ballot stuffing form of collusion, a colluding group inflates each other's reputations which then allows them to use the good reputation to attack other system peers.…”

Section: Reputation Aggregationmentioning

confidence: 99%

The design of a reliable reputation system

Swamynathan

Almeroth

Zhao

2010

Electron Commer Res

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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.

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“…The eBay rating system used for choosing trading partners where each participant in a transaction can vote (−1, 0, 1) on their counterpart, the Amazon customer review system and the Slashdot self-moderation of posts [1] are all systems where the ratings are provided by nodes but are stored in a central database. Many such reputation systems have been studied in the context of online communities and marketplaces [2,3,4].…”

Section: Related Workmentioning

confidence: 99%

Dynamic Self-management of Autonomic Systems: The Reputation, Quality and Credibility (RQC) Scheme

Garg

Battiti

Costanzi

2005

Lecture Notes in Computer Science

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Abstract. In this paper, we present a feedback-based system for managing trust and detecting malicious behavior in autonomically behaving networks. Like other distributed trust management systems, nodes rate the interactions they have with other nodes and this information is stored in a distributed fashion.Two crucial insights motivate our work. We recognize as separate entities the trust placed in a node, reputation, and the trust placed in the recommendations made by a node, credibility. We also introduce the concept of quality of a trust rating. Together, these two factors enhance the ability of each node to decide how much confidence it can place in a rating provided to it by a third party.We implement our scheme on a structured P2P network, Pastry, though our results can be extended to generic autonomic communication systems. Experimental results considering different models for malicious behavior indicate the contexts in which the RQC scheme performs better than existing schemes.

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Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior

Cited by 473 publications

References 5 publications

The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

The Digitization of Word of Mouth: Promise and Challenges of Online Feedback Mechanisms

The design of a reliable reputation system

Dynamic Self-management of Autonomic Systems: The Reputation, Quality and Credibility (RQC) Scheme

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