PSH: A private and shared history-based incentive mechanism

Fully decentralized peer-to-peer (P2P) systems do not have a central control mechanism. Thus, different forms of control mechanisms are required to deal with selfish peers. One type of selfish behavior is the consumption of resources without providing sufficient resources. Therefore, incentive schemes encourage peers to share resources while punishing selfish peers. A well-known example of an incentive scheme is Tit-for-Tat (TFT), as used in BitTorrent. With this scheme, a peer can only consume as much resources as it provides. TFT is resilient to collusion due to relying on private histories only. However, TFT can only be applied to peers with direct reciprocity. This paper presents a private and shared history (PSH) based incentive mechanism, which supports transitive relations (indirect reciprocity). Furthermore, it is resilient to collusion and it combines private and shared histories in an efficient manner. The PSH approach uses a shared history for identifying transitive relations. Those relations are verified using private histories. Simulations show that the PSH mechanism has a higher transaction success ratio than TFT

On the α-Sensitivity of Nash Equilibria in PageRank-Based Network Reputation Games

Abstract. Web search engines use link-based reputation systems (e.g. PageRank) to measure the importance of web pages, giving rise to the strategic manipulations of hyperlinks by spammers and others to boost their web pages ’ reputation scores. Hopcroft and Sheldon [10] study this phenomenon by proposing a network formation game in which nodes strategically select their outgoing links in order to maximize their PageRank scores. They pose an open question in [10] asking whether all Nash equilibria in the PageRank game are insensitive to the restart probability α of the PageRank algorithm. They show that a positive answer to the question would imply that all Nash equilibria in the PageRank game must satisfy some strong algebraic symmetry, a property rarely satisfied by real web graphs. In this paper, we give a negative answer to this open question. We present a family of graphs that are Nash equilibria in the PageRank game only for certain choices of α.

Monitoring and Reputation Mechanisms for Service Level Agreements

A Service Level Agreement (SLA) is an electronic contract between a service user and a provider, and specifies the service to be provided, Quality of Service (QoS) properties that must be maintained by a provider during service provision (generally defined as a set of Service Level Objectives (SLOs)), and a set of penalty clauses specifying what happens when service providers fail to deliver the QoS agreed. Although significant work exists on how SLOs may be specified and monitored, not much work has focused on actually identifying how SLOs may be impacted by the choice of specific penalty clauses. A trusted mediator may be used to resolve conflicts between the parties involved. The objectives of this work are to: (i) identify classes of penalty clauses that can be associated with an SLA; (ii) define how to specify penalties in an extension of WS-Agreement; and (iii) specify to what extent penalty clauses can be enforced based on monitoring of an SLA

Evaluating trustworthiness through monitoring: The foot, the horse and the elephant

This paper presents a framework for trust evaluation through monitoring, in particular, to address the question of how to derive trust from observations of certain properties. We propose a trust model based on subjective logic to represent trust through the notion of an opinion and to include aspects of uncertainty in a systematic fashion. Moreover, we analyze requirements for opinion generators and introduce novel parameterized generators that capture the requirements for opinion generators much better than current generators do. In addition, we show how a decision can be made based on trust monitoring within a certain context. The proposed trust evaluation framework is demonstrated with a case study of a Body Area Sensor Network. The results and examples show that the opinion generators can effectively work with various types of properties, including dependability, security and functionality related properties