Performance of peer-to-peer resource sharing networks depends upon the level of cooperation of the participants. To date, cash-based systems have seemed too complex, while lighter-weight credit mechanisms have not provided strong incentives. We propose exchange-based mechanisms for providing incentives for cooperation in peer-to-peer file sharing networks. Peers give higher service priority to requests from peers that can provide a simultaneous and symmetric service in return. We generalize this approach to #-way exchanges among rings of peers and present a search algorithm for locating such rings. We have used simulation to analyze the effect of exchanges on performance. Our results show that exchange-based mechanisms can provide strong incentives for sharing, offering significant improvements in service times for sharing users compared to free-riders, without the problems and complexity of cash- or credit-based systems.

Decentralized Peer to Peer (P2P) networks offer both opportunities and threats. Its open and decentralized nature makes it extremely susceptible to malicious users spreading harmful content like viruses, trojans or, even just wasting valuable resources of the network. In order to minimize such threats, the use of community-based reputations as trust measurements is fast becoming a de-facto standard. The idea is to dynamically assign each peer a trust rating based on its performance in the network and store it at a suitable place. Any peer wishing to interact with another peer can make an informed decision based on such a rating.

Reputation systems have been popular in estimating the trustworthiness and predicting the future behavior of nodes in a large-scale distributed system where nodes may transact with one another without prior knowledge or experience. One of the fundamental challenges in distributed reputation management is to understand vulnerabilities and develop mechanisms that can minimize the potential damages to a system by malicious nodes. In this paper, we identify three vulnerabilities that are detrimental to decentralized reputation management and propose TrustGuard -- safeguard framework for providing a highly dependable and yet efficient reputation system. First, we provide a dependable trust model and a set of formal methods to handle strategic malicious nodes that continuously change their behavior to gain unfair advantages in the system. Second, a transaction based reputation system must cope with the vulnerability that malicious nodes may misuse the system by flooding feedbacks with fake transactions. Third, but not least, we identify the importance of filtering out dishonest feedbacks when computing reputation-based trust of a node, including the feedbacks filed by malicious nodes through collusion. Our experiments show that, comparing with existing reputation systems, our framework is highly dependable and effective in countering malicious nodes regarding strategic oscillating behavior, flooding malevolent feedbacks with fake transactions, and dishonest feedbacks.

Grid computing has excited many with the promise of access to huge amounts of resources distributed across the globe. However, there are no largely adopted solutions for automatically assembling grids, and this limits the scale of today's grids. Some argue that this is due to the overwhelming complexity of the proposed economy-based solutions. Peer-to-peer grids have emerged as a less complex alternative. We are currently deploying OurGrid, one such peerto -peer grid. OurGrid is a CPU-sharing grid that targets Bag-of-Tasks applications (i.e. parallel applications whose tasks are independent). In order to ease system deployment, OurGrid is based on a very lightweight autonomous reputation scheme.

Abstract. Trust management is currently being tackled from two different perspectives: a “strong and crisp ” approach, where decisions are founded on logical rules and verifiable properties encoded in digital credentials, and a “soft and social ” approach, based on reputation measures gathered and shared by a distributed community. We analyze the differences between the two models of trust and argue that an integrated approach would improve significantly trust management systems. We support our claim with real world scenarios and illustrate how the two models are integrated in PROTUNE, the core policy specification language of the network of excellence REWERSE. 1

The problem of encouraging trustworthy behavior in P2P online communities by managing peers' reputations has drawn a lot of attention recently. However, most of the proposed solutions exhibit the following two problems: huge implementation overhead and unclear trust related model semantics. In this paper we show that a simple probabilistic technique, maximum likelihood estimation namely, can reduce these two problems substantially when employed as the feedback aggregation strategy. Thus, no complex exploration of the feedback is necessary. Instead, simple, intuitive and e#cient probabilistic estimation methods su#ce.

This paper presents a token-based accounting mechanism that alleviates the free riding problem in P2P networks. The approach is complemented by distributed pricing as a flexible and viable scheme to incite users to share valuable content and to efficiently balance requests among all peers based on economic decisions.

Sensor networks are often subject to physical attacks. Once a node’s cryptographic key is compromised, an attacker may completely impersonate it and introduce arbitrary false information into the network. Basic cryptographic mechanisms are often not effective in this situation. Most techniques to address this problem focus on detecting and tolerating false information introduced by compromised nodes. They cannot pinpoint exactly where the false information is introduced and who is responsible for it. In this article, we propose an application-independent framework for accurately identifying compromised sensor nodes. The framework provides an appropriate abstraction of applicationspecific detection mechanisms and models the unique properties of sensor networks. Based on the framework, we develop alert reasoning algorithms to identify compromised nodes. The algorithm assumes that compromised nodes may collude at will. We show that our algorithm is optimal in the sense that it identifies the largest number of compromised nodes without introducing false

In this paper we propose a reputation management framework for large-scale peer-to-peer (P2P) networks, wherein all nodes are assumed to behave selfishly. The proposed framework has several advantages. It enables a form of virtual currency, such that the reputation of nodes is a measure of their wealth. The framework is scalable and provides protection against attacks by malicious nodes. The above features are achieved by developing trusted communities of nodes whose members trust each other and cooperate to deal with the problem of nodes' selfishness and possible maliciousness.

The problem of encouraging trustworthy behavior in P2P online communities by managing peers' reputations has drawn a lot of attention recently. However, most of the proposed solutions exhibit the following two problems: huge implementation overhead and unclear trust related model semantics. In this paper we show that a simple probabilistic technique, maximum likelihood estimation namely, can reduce these two problems substantially when employed as the feedback aggregation strategy. Thus, no complex exploration of the feedback is necessary. Instead, simple, intuitive and e#cient probabilistic estimation methods su#ce.