1 Introduction Establishing trust is a fundamental problem in distributedsystems. Peer-to-peer systems, in which service functionality is distributed across clients, eliminate the cen-tralized components that have traditionally functioned as de facto trust brokers, and consequently exacerbate trust-related problems. When peers lack meaningful measures on which to base trust decisions, they end up receivingservices from untrustworthy peers, with e ffects that canrange from wasted resources on mislabeled content to

An emerging paradigm in peer-to-peer (P2P) networks is to explicitly consider incentives as part of the protocol design in order to promote good (or discourage bad) behavior. However, effective incentives are hampered by the challenges of a P2P environment, e.g. transient users and no central authority. In this paper, we quantify these challenges, reporting the results of a month-long measurement of millions of users of the BitTorrent file sharing system. Surprisingly, given BitTorrent’s popularity, we identify widespread performance and availability problems. These measurements motivate the design and implementation of a new, one hop reputation protocol for P2P networks. Unlike digital currency systems, where contribution information is globally visible, or titfor-tat, where no propagation occurs, one hop reputations limit propagation to at most one intermediary. Through trace-driven analysis and measurements of a deployment on PlanetLab, we find that limited propagation improves performance and incentives relative to BitTorrent. 1

Peer-to-peer filesharing is now commonplace and its traffic now dominates bandwidth consumption at many Internet peering points. Recent studies indicate that much of this filesharing activity involves corrupt and polluted files. This paper describes Credence, a new object-based reputation system, and shows how it can counteract content pollution in peer-to-peer filesharing networks. Credence allows honest peers to assess the authenticity of online content by securely tabulating and managing endorsements from other peers. We employ a novel voter correlation scheme to weigh the opinions of peers, which gives rise to favorable incentives and system dynamics. We present simulation results indicating that our system is scalable, efficient, and robust.

Reputation mechanisms harness the bi-directional communication capabilities of the Internet in order to engineer large-scale word-of-mouth networks. Best known so far as a technology for building trust and fostering cooperation in online marketplaces, such as eBay, these mechanisms are poised to have a much wider impact on organizations. This paper surveys our progress in understanding the new possibilities and challenges that these mechanisms represent. It discusses some important dimensions in which Internet-based reputation mechanisms differ from traditional word-of-mouth networks and surveys the most important issues related to their design, evaluation, and use. It provides an overview of relevant work in game theory and economics on the topic of reputation. It discusses how this body of work is being extended and combined with insights from computer science, marketing, and psychology in order to take into consid-eration the special properties of online environments. Finally, it identifies opportunities that this new area presents for information systems research. 1

P2P systems inherently have high scalability, robustness and fault tolerance because there is no centralized server and the network self-organizes itself. This is achieved at the cost of higher latency for locating the resources of interest in the P2P overlay network. Internet telephony can be viewed as an application of P2P architecture where the participants form a self-organizing P2P overlay network to locate and communicate with other participants. We propose a pure P2P architecture for the Session Initiation Protocol (SIP)-based IP telephony systems. Our P2P-SIP architecture supports basic user registration and call setup as well as advanced services such as offline message delivery, voice/video mails and multi-party conferencing.

We study online social networks in which relationships can be either positive (indicating relations such as friendship) or negative (indicating relations such as opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. We find that the signs of links in the underlying social networks can be predicted with high accuracy, using models that generalize across this diverse range of sites. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, shedding light on theories of balance and status from social psychology; they also suggest social computing applications by which the attitude of one user toward another can be estimated from evidence provided by their relationships with other members of the surrounding social network.

The open and anonymous nature of a P2P network makes it an ideal medium for attackers to spread malicious content. In this paper, we propose a reputation-based trust management system for P2P networks that aims to build confidence among the good members of the community and identify the malicious ones. The proposed system is simple and efficient in design and can be integrated into most first generation P2P systems easily. A diverse set of simulation experiments conducted to test the performance of the system show that it can be highly effective in preventing the spread of malicious content. The proposed system has other potential benefits as well, such as supporting the detection of free riders in a file sharing application.

Abstract: Peer-to-Peer (P2P) reputation systems are essential to evaluate the trustworthiness of participating peers and to combat the selfish, dishonest, and malicious peer behaviors. The system collects locally-generated peer feedbacks and aggregates them to yield the global reputation scores. Surprisingly, most previous work ignored the distribution of peer feedbacks. We use a trust overlay network (TON) to model the trust relationships among peers. After examining the eBay transaction trace of over 10,000 users, we discovered a power-law distribution in user feedbacks. Our mathematical analysis justifies that power-law distribution is applicable to any dynamically growing P2P systems, either structured or unstructured. We develop a robust and scalable P2P reputation system, PowerTrust, to leverage the power-law feedback characteristics. The PowerTrust system dynamically selects small number of power nodes that are most reputable using a distributed ranking mechanism. By using a look-ahead random walk strategy and leveraging the power nodes, the PowerTrust significantly improves in global reputation accuracy and aggregation speed. PowerTrust is adaptable to dynamics in peer joining and leaving and robust to disturbance by malicious peers. Through P2P network simulation experiments, we find significant performance gains in using PowerTrust. This power-law guided reputation system design proves to achieve high query success rate in P2P file-sharing applications. The system also reduces the total job makespan and failure rate in large-scale, parameter-sweeping P2P Grid applications. Keywords: Peer-to-Peer system, overlay network, distributed hash table, reputation system, eBay trace dataset, distributed file sharing, P2P Grids, PSA benchmark, and system scalability.

Abstract—In scheduling a large number of user jobs for parallel execution on an open-resource Grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in Grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure Grid job execution under different risky conditions. We report the simulated Grid performances of these new Grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, Grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area Grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant Min-Min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of Grid computing services. These riskresilient job scheduling schemes can upgrade Grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky Grid computing environment. Index Terms—Grid computing, job scheduling heuristics, genetic algorithm, replication scheduling, risk resilience, NAS and PSA benchmarks, performance metrics, distributed supercomputing. 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.