Manipulating Scrip Systems: Sybils and Collusion

Game-theoretic analyses of distributed and peer-to-peer systems typically use the Nash equilibrium solution concept, but this explicitly excludes the possibility of strategic behavior involving more than one agent. We examine the effects of two types of strategic behavior involving more than one agent, sybils and collusion, in the context of scrip systems where agents provide each other with service in exchange for scrip. Sybils make an agent more likely to be chosen to provide service, which generally makes it harder for agents without sybils to earn money and decreases social welfare. Surprisingly, in certain circumstances it is possible for sybils to make all agents better off. While collusion is generally bad, in the context of scrip systems it actually tends to make all agents better off, not merely those who collude. These results also provide insight into the effects of allowing agents to advertise and loan money. While many extensions of Nash equilibrium have been proposed that address collusion and other issues relevant to distributed and peer-to-peer systems, our results show that none of them adequately address the issues raised by sybils and collusion in scrip systems.Comment: 20 pages, 5 figures. To appear in the Proceedings of The First Conference on Auctions, Market Mechanisms and Their Applications (AMMA '09

Neuropsychological constraints to human data production on a global scale

Which are the factors underlying human information production on a global level? In order to gain an insight into this question we study a corpus of 252-633 Million publicly available data files on the Internet corresponding to an overall storage volume of 284-675 Terabytes. Analyzing the file size distribution for several distinct data types we find indications that the neuropsychological capacity of the human brain to process and record information may constitute the dominant limiting factor for the overall growth of globally stored information, with real-world economic constraints having only a negligible influence. This supposition draws support from the observation that the files size distributions follow a power law for data without a time component, like images, and a log-normal distribution for multimedia files, for which time is a defining qualia.Comment: to be published in: European Physical Journal

The Sybil Attack in Participatory Sensing: Detection and Analysis

[[abstract]]Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. Nevertheless, one of the most important issues and misgiving about participatory sensing applications is security. Different from other participatory sensing application challenges who consider user privacy and data trustworthiness, we consider network trustworthiness problem namely Sybil attacks in participatory sensing. Sybil attacks is a particularly harmful attack against participatory sensing application, where Sybil attacks focus on creating multiple online user identities called Sybil identities and try to achieve malicious results through these identities. In this paper, we proposed a Hybrid Trust Management (HTM) framework for detecting and analyze Sybil attacks in participatory sensing network. Our HTM was proposed for performing Sybil attack characteristic check and trustworthiness management system to verify coverage nodes in the participatory sensing. To verify the proposed framework, we are currently developing the proposed scheme on OMNeT++ network simulator in multiple scenarios to achieve Sybil identities detection in our simulation environment.[[notice]]補正完

Detecting Occasional Reputation Attacks on Cloud Services

Abstract. Cloud service consumers ’ feedback is a good source to assess the trustworthiness of cloud services. However, it is not unusual that a trust management system experiences malicious behaviors from its users. Although several techniques have been proposed to address trust management in cloud environments, the issue of how to detect occasional reputation attacks on cloud services is still largely overlooked. In this paper, we introduce an occasional attacks detection model that recognizes misleading trust feedbacks from occasional collusion and Sybil attacks and adjusts trust results for cloud services that have been affected by these malicious behaviors. We have collected a large collection of consumer’s trust feedbacks given on real-world cloud services (over ten thousand records) to evaluate and demonstrate the applicability of our approach and show the capability of detecting such malicious behaviors