On the Impact of Witness-Based Collusion in Agent Societies

Salehi‐Abari, Amirali; White, Tony

doi:10.1007/978-3-642-11161-7_6

Cited by 5 publications

(2 citation statements)

References 12 publications

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“…To discover collusion groups, the most difficult task is to identify group members. Salehi-Abari et al [16] defined collusion as "A collaborative activity that gives to members of a colluding group benefits they would not be able to gain as individuals". The definition shows two essential characteristics of collusion groups.…”

Section: Introductionmentioning

confidence: 99%

GroupTie

Xie

Zhu

2014

Proceedings of the 2014 ACM Conference on Security and Privacy in Wireless &Amp; Mobile Networks

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The current centralized application (or app) markets provide convenient ways to distribute mobile apps. Their vendors maintain rating systems, which allow customers to leave ratings and reviews. Since positive ratings and reviews can lead to more downloads/installations and hence more monetary benefit, the rating systems have become a target of manipulation by some collusion groups hired by app developers. In this paper, we thoroughly analyze the features of hidden collusion groups and propose a novel method called GroupTie to narrow down the suspect list of collusive reviewers for further investigation by app stores. As members of a hidden collusion group have to work together more frequently and their ratings often deviate more from apps' quality, collusive actions will enhance their relation over time. We build a relation graph named tie graph and detect collusion groups by applying graph clustering. Simulation results show that the precision of GroupTie approaches to 99.70% and the recall is about 91.50%. We also apply our method to detect hidden collusion groups among the reviewers of 89 apps in Apple's China App Store. A large number of reviewers are discovered belonging to a large collusion group and several small groups.

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Section: Introductionmentioning

confidence: 99%

GroupTie

Xie

Zhu

2014

Proceedings of the 2014 ACM Conference on Security and Privacy in Wireless &Amp; Mobile Networks

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“…Recently, researchers have identified the existence of cheaters (exploitation) in artificial societies employing trust and reputation models (Kerr and Cohen 2009;Salehi-Abari and White 2009b;2009a). (Kerr and Cohen 2009) examined the security of several e-commerce marketplaces employing a trust and reputation system.…”

Section: Introductionmentioning

confidence: 99%

Trust Models and Con-Man Agents: From Mathematical to Empirical Analysis

Salehi‐Abari

White

2010

AAAI

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Recent work has demonstrated that several trust and reputation models can be exploited by malicious agents with cyclical behaviour. In each cycle, the malicious agent with cyclical behaviour first regains a high trust value after a number of cooperations and then abuses its gained trust by engaging in a bad transaction. Using a game theoretic formulation, Salehi-Abari and White have proposed the AER model that is resistant to exploitation by cyclical behaviour. Their simulation results imply that FIRE, Regret, and a model due to Yu and Singh, can always be exploited with an appropriate value for the period of cyclical behaviour. Furthermore, their results demonstrate that this is not so for the proposed adaptive scheme. This paper provides a mathematical analysis of the properties of five trust models when faced with cyclical behaviour of malicious agents. Three main results are proven. First, malicious agents can always select a cycle period that allows them to exploit the four models of FIRE, Regret, Probabilistic models, and Yu and Singh indefinitely. Second, malicious agents cannot select a single, finite cycle period that allows them to exploit the AER model forever. Finally, the number of cooperations required to achieve a given trust value increases monotonically with each cycle. In addition to the mathematical analysis, this paper empirically shows how malicious agents can use the theorems proven in this paper to mount efficient attacks on trust models.

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The relationship of trust, demand, and utility: Be more trustworthy, then i will buy more

Salehi‐Abari

White

2010

2010 Eighth International Conference on Privacy, Security and Trust

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Abstract-In many marketplaces consumers may interact with more than one supplier in order to achieve a particular goal. This paper takes the position that trust mediates agent interactions and that agent demands are likely to increase with increasing trust. Furthermore, trust of one agent in another is updated based upon a trust model. The model proposed in this paper is one in which agent demands can be divided amongst suppliers with the division determined by trust. The model evaluates five prominent trust models in a hypothetical marketplace and demonstrates empirically that FIRE, Regret, Probabilistic Trust Models and a model due to Yu and Singh can be exploited such that a consumer agent will continue to interact with a malicious agent despite periodic contract defaults by the malicious agent. Empirical studies support the hypothesis that adaptive trust models such as AER are resistant to this type of exploitation and discourage periodic or cyclical defaulting on contractual obligations.

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On the Impact of Witness-Based Collusion in Agent Societies

Cited by 5 publications

References 12 publications

GroupTie

GroupTie

Trust Models and Con-Man Agents: From Mathematical to Empirical Analysis

The relationship of trust, demand, and utility: Be more trustworthy, then i will buy more

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