Reputation-based pricing of P2P services

Jurca, Radu; Faltings, Boi

doi:10.1145/1080192.1080206

Cited by 30 publications

(14 citation statements)

References 16 publications

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“…The indirect reciprocity schemes [8][9] [10] are often called reputation-based mechanisms. However, these schemes rely on secondhand observations and thus must confront trust issues and complex computation overhead.…”

Section: B Combating Malicious and Free-riding Behaviormentioning

confidence: 99%

“…Previous work either rely on centralized infrastructure or globe reputation knowledge [8][9][10][11][12] to combat malicious and free-riding behavior, which is not proper to large scale P2P system with millions peers and is prohibitive for propagating reputation values, or they [13] [15] do not realize the amazing characteristic of P2P content sharing system is not only lying in locating the proper contents, but also get the content with fast speed. Therefore, in their topology adaptation way, the trust of a peer does not take transmission into consideration.…”

Section: Introductionmentioning

confidence: 99%

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TARC: A Novel Topology Adaptation Algorithm Based on Reciprocal Contribution in Unstructured P2P Networks

Shuang

Yang

2009

2009 International Conference on Parallel Processing Workshops

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The autonomous behaviors of peers are usually neglected in the topology formation and adaptation in P2P network, thus a large number of Free-rider and Malicious peers would reside in the center of network, which degrades the user perceived QoE and overall network performance. In this paper, a novel and fully distributed approach Topology Adaptation algorithm based on Reciprocal Contribution (TARC) is proposed to address such problem. In our paradigm, the topology would be periodical optimized according to Reciprocal Contribution Capacity (RCC) of peers. The RCC reflects the historical behavior of a peer with regard to its content provision and transmission capability as well as nodes locating capability. Peer that possesses higher RCC, namely contributor, as it were more cooperative would be easily and likely to establish connections with others. On the other hand, malicious and free-riding nodes would be recognized and forced to the marginal of P2P network. It is shown through experiments that TARC effectively suppress the fake services and prevent non-contribution peer from occupying the center of network. What's more, there is a significant improvement in term of overall search quality and downloading time, for contributing peers in a network that applies our algorithm.

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Section: B Combating Malicious and Free-riding Behaviormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

TARC: A Novel Topology Adaptation Algorithm Based on Reciprocal Contribution in Unstructured P2P Networks

Shuang

Yang

2009

2009 International Conference on Parallel Processing Workshops

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“…Jurca and Faltings extend the analysis of reputation-based pricing to service oriented environments where providers with different capabilities coexist [10,11]. Their results are based on two observations.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, the reputation information obtained by aggregating the feedback from the immediate past offers an accurate estimation of the real QoS delivered by the provider. In [10], client's pay the price corresponding to yesterday's reputation. In [11], a reputation-based SLA allows clients to be refunded at the end of a given time period if the provider's reputation is lower than promised.…”

Section: Related Workmentioning

confidence: 99%

“…Occasional failures happen against the best intention of service providers; excluding such providers from the market would be unfair and inefficient. Instead, reputation information can be used to approximate the real quality of service (QoS) delivered by a provider, and consequently, to dynamically scale the price paid for service [10]. Such a flexible pricing scheme allows service providers with different characteristics to reach the best compromise between delivered quality (hence revenue) and costs.…”

Section: Introductionmentioning

confidence: 99%

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Using CHI-scores to reward honest feedback from repeated interactions

Jurca¹,

Faltings²

2006

Proceedings of the Fifth International Joint Conference on Autonomous Agents and Multiagent Systems

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Online communities increasingly rely on reputation information to foster cooperation and deter cheating. As rational agents can often benefit from misreporting their observations, explicit incentives must be created to reward honest feedback. Reputation side-payments (e.g., agents get paid for submitting feedback) can be designed to make truth-telling optimal. In this paper, we present a new sidepayment scheme adapted for settings where agents repeatedly submit feedback. We rate the feedback set of an agent, rather than individual reports. The CHI-Score of the feedback set is computed based on a Chi-square independence test that assesses the correlation between the agent's feedback and the feedback submitted by the rest of the community. The mechanism has intuitive appeal and generates significantly lower costs than existing incentive-compatible reporting mechanisms.

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Reputation management in collaborative computing systems

Arenas¹,

Aziz²,

Silaghi³

2010

Security Comm Networks

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In collaborative systems, a set of organizations shares their computing resources, such as compute cycles, storage space or on-line services, in order to establish Virtual Organizations (VOs) aimed at achieving common tasks. The formation and operation of Virtual Organizations involve establishing trust among their members and reputation is one measure by which such trust can be quantified and reasoned about. In this paper, we contribute to research in the area of trust for collaborative computing systems along two directions: first, we provide a survey on the main reputation-based systems that fulfil the trust requirements for collaborative systems, including reputation systems designed for e-commerce, agent-based environments, and Peer-to-Peer computing and Grid-based systems. Second, we present a model for reputation management for Grid Virtual Organizations that is based on utility computing and that can be used to rate users according to their resource usage and resources and their providers according to the quality of service they deliver. We also demonstrate, through Grid simulations, how the model can be used in improving completion and welfare in Virtual Organizations.as Grid-based supply chains [2], among others. In such collaborative systems, trust management is a fundamental problem as resource owners must share their resources with unknown organizations as well as ensuring that all users abide by the VO agreement to which the resources have been allocated. This paper investigates how to exploit reputation systems in the management of VOs. Reputation is one measure by which trust among different members of a In this section, we develop a general utility-based reputation model for VOs, which will be used later to manage reputation in service-oriented VOs. Our reputation model is based on the model described in Reference [3]. The model was initially devised for service-oriented computing in grid systems and improves the models presented in Subsection 2.5. Central to our model is the notion of an organization. The set of all organizations is denoted by Org. We keep REPUTATION MANAGEMENT IN COLLABORATIVE COMPUTING SYSTEMS 553 Aggregating the reputation of an entity over all its consumers within a VO produces the reputation of the entity in the VO with respect to a particular issue of interest.Srv rep eic == rep eic [Time, VOUser, Srv, {QoS}] Srv rep ei == rep ei [Time, Srv, {QoS}] Srv rep e == rep ei [Time, Srv] Srv rep == rep [Time, Srv] REPUTATION MANAGEMENT IN COLLABORATIVE COMPUTING SYSTEMS 557 User rep eic == rep eic [Time, Srv, VOUser, {Usage}] User rep ei == rep ei [Time, VOUser, {Usage}] User rep e == rep ei [Time, VOUser] User rep == rep [Time, VOUser]

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Reputation-based pricing of P2P services

Cited by 30 publications

References 16 publications

TARC: A Novel Topology Adaptation Algorithm Based on Reciprocal Contribution in Unstructured P2P Networks

TARC: A Novel Topology Adaptation Algorithm Based on Reciprocal Contribution in Unstructured P2P Networks

Using CHI-scores to reward honest feedback from repeated interactions

Reputation management in collaborative computing systems

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