Enhancing peer-to-peer collaboration using trust

Distributed systems generally require their component parts to interact cooperatively, in order for the system as a whole to function effectively. For any given activity, there are typically several alternative components that have the required capabilities. In decentralised systems, where there is no overarching control, individual components are responsible for selecting other components with which to cooperate. However, the candidate components may be unreliable or dishonest, and are typically locally controlled. Such decentralised systems can be viewed as multi-agent systems, comprising autonomous agents that must cooperate for the system to be effective. Peer-to-peer (P2P) systems are a subclass of decentralised distributed systems, in which not only is there no overarching control, but neither is there any hierarchy of control, power, or responsibly among the system components. Selecting appropriate peers to cooperate with is a challenging problem, since the candidate peers are autonomous and may be unreliable or dishonest. Peers need a mechanism for task delegation that takes the uncertainly of interactions into account. In this paper we present a mechanism, called Mdt-r, that enables peers to delegate activities appropriately, using trust and the recommendations of other peers to meet individual preferences, such as minimising risk and maximising quality.     

CollectCast: A peer-to-peer service for media streaming

We present CollectCast, a peer-to-peer (P2P) service for media streaming where a receiver peer is served by multiple sender peers. CollectCast operates at the application level but infers underlying network properties to correlate end-to-end connections between peers. The salient features of CollectCast include: (1) a novel multisender selection method that exploits the performance correlation and dependency among connections between different candidate senders and the receiver, (2) a customization of network tomography techniques and demonstration of improved practicality and efficiency, and (3) an aggregation-based P2P streaming mechanism that sustains receiver-side quality in the presence of sender/network dynamics and degradation. We have performed both real-world (on PlanetLab) and simulation evaluation of CollectCast. Our simulation results show that for a receiver, CollectCast makes better selection of multiple senders than other methods that do not infer underlying network properties. Our PlanetLab experiments are performed using a P2P media streaming application (called PROMISE) which we developed on top of CollectCast. Both packet-level and frame-level performance of MPEG-4 video streaming demonstrates the practicality and effectiveness of CollectCast.     

Context-aware trust negotiation in peer-to-peer service collaborations

Service-oriented architecture (SOA) and Software as a Service (SaaS) are the latest hot topics to software manufacturing and delivering, and attempt to provide a dynamic cross-organisational business integration solution. In a dynamic cross-organisational collaboration environment, services involved in a business process are generally provided by different organisations, and lack supports of common security mechanisms and centralized management middleware. On such occasions, services may have to achieve middleware functionalities and achieve business objectives in a pure peer-to-peer fashion. As the participating services involved in a business process may be selected and combined at run time, a participating service may have to collaborate with multiple participating services which it has no pre-existing knowledge in prior. This introduces some new challenges to traditional trust management mechanisms. Automated Trust Negotiation (ATN) is a practical approach which helps to generate mutual trust relationship for collaborating principals which may have no pre-existing knowledge about each other without in a peer-to-peer way. Because credentials often contain sensitive attributes, ATN defines an iterative and bilateral negotiation process for credentials exchange and specifies security policies that regulate the disclosure of sensitive credentials. Credentials disclosure in the iterative process may follow different orders and combinations, each of which forms a credential chain. It is practically desirable to identify the optimal credential chain that satisfies certain objectives such as minimum release of sensitive information and minimum performance penalty. In this paper we present a heuristic and context-aware algorithm for identifying the optimal chain that uses context-related knowledge to minimize 1) the release of sensitive information including both credentials and policies and 2) the cost of credentials retrieving. Moreover, our solution offers a hierarchical method for protecting sensitive policies and provides a risk-based strategy for handling credential circular dependency. We have implemented the ATN mechanisms based on our algorithm and incorporated them into the CROWN Grid middleware. Experimental results demonstrate their performance-related advantages over other existing solutions.

An integrated trust and reputation model for open multi-agent systems

Trust and reputation are central to effective interactions in open multi-agent systems (MAS) in which agents, that are owned by a variety of stakeholders, continuously enter and leave the system. This openness means existing trust and reputation models cannot readily be used since their performance suffers when there are various (unforseen) changes in the environment. To this end, this paper presents FIRE, a trust and reputation model that integrates a number of information sources to produce a comprehensive assessment of an agent’s likely performance in open systems. Specifically, FIRE incorporates interaction trust, role-based trust, witness reputation, and certified reputation to provide trust metrics in most circumstances. FIRE is empirically evaluated and is shown to help agents gain better utility (by effectively selecting appropriate interaction partners) than our benchmarks in a variety of agent populations. It is also shown that FIRE is able to effectively respond to changes that occur in an agent’s environment.     

Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

HyperTree for self-stabilizing peer-to-peer systems

Peer-to-peer systems are prone to faults; Therefore, it is extremely important to design peer-to-peer systems that automatically regain consistency or, in other words, are self-stabilizing. In order to achieve the above, we present a deterministic structure that defines the entire (IP) pointers structure among the machines, for every n machines; i.e., defines the next hop for the insert, delete, and search procedures of the peer-to-peer system. Thus, the consistency of the system is easily defined, monitored, verified, and repaired. We present the HyperTree (distributed) structure, which supports the peer-to-peer procedures while ensuring that the out-degree and the in-degree (the number of outgoing/ incoming pointers) are b log _b n where n is the actual number of machines and b is an integer parameter greater than 1. Moreover, the HyperTree ensures that the maximal number of hops involved in each procedure is bounded by log _b n. A self-stabilizing peer-to- peer distributed algorithm based on the HyperTree is presented. This work was partially supported by IBM Faculty Award, NSF Grant 0098305, the Israeli Ministry of Trade and Industry, the Rita Altura Trust Chair in Computer Sciences and the Lynne and William Frankel Center for Computer Sciences. The work was done while Ronen I. Kat was a PhD student at Ben-Gurion University of the Negev. An preliminary version was published in the proceedings of the third IEEE International Symposium on Network Computing and Applications (NCA’04).     

Strategies for peer-to-peer downloading

An advantage of peer-to-peer applications is that files can be shared without concentrated load on file servers. This note proposes deterministic techniques for splitting a files into segments so that, for certain restricted cases of arrival and server rates, users may copy files from one another without fetching the data from file servers.     

P2P系统的信任研究

由于P2P系统的开放、匿名等特点,使得P2P系统对节点缺乏约束机制,节点间缺乏信任。针对以上问题,本文提出了一种新的P2P系统信任模型,该模型根据系统中节点的历史交易情况和系统中其它节点的推荐计算节点的信任度,节点根据计算的结果决定是否进行交易。仿真试验及分析表明,该模型能有效地评估节点的信任度,隔离恶意节点,提高下载成功率。     

Skyframe: a framework for skyline query processing in peer-to-peer systems

This paper looks at the processing of skyline queries on peer-to-peer (P2P) networks. We propose Skyframe, a framework for efficient skyline query processing in P2P systems, which addresses the challenges of quick response time, low network communication cost and query load balancing among peers. Skyframe consists of two querying methods: one is optimized for network communication while the other focuses on query response time. These methods are different in the way in which the query search space is defined. In particular, the first method uses a high dominating point that has a large dominating region to prune the search space to achieve a low cost in network communication. On the other hand, the second method relaxes the search space in order to allow parallel query processing to speed up query response. Skyframe achieves query load balancing by both query load conscious data space splitting/merging during the join/departure of nodes and dynamic load migration. We further show how to apply Skyframe to both the P2P systems supporting multi-dimensional indexing and the P2P systems supporting single-dimensional indexing. Finally, we have conducted extensive experiments on both real and synthetic data sets over two existing P2P systems: CAN (Ratnasamy in A scalable content-addressable network. In: Proceedings of SIGCOMM Conference, pp. 161–172, 2001) and BATON (Jagadish et al. in A balanced tree structure for peer-to-peer networks. In: Proceedings of VLDB Conference, pp. 661–672, 2005) to evaluate the effectiveness and scalability of Skyframe.     

Reliable peers and useful resources: Searching for the best personalised learning path in a trust- and recommendation-aware environment

The evolution of the Web 2.0 and the intensive use of peer-to-peer networks allow us to access more and more information from disparate data sources than in the past, thus making life-long learning more effective. In this scenario, a critical issue still remains to be addressed: the reliability of resources, whether they can be recommended as useful and the reliability of peers, whether it is possible to trust them as providers. We propose to integrate these concepts with e-learning, proposing a model for searching for personalised and useful learning paths suggested by reliable (trusted) peers. We performed simulations on the Merlot data set enhanced with information extracted from Advogato, Epinions and Ariadne data sets, testing the efficiency and effectiveness of the proposed approach.     

Reputation based friend-to-friend networks

Friend-to-Friend networks are a form of Peer-to-Peer networks that provide anonymity of the users who share data. Turtle is an example of such a network, where a peer only connects to real life friends. In this paper we propose an extension of Turtle for connecting to other nodes based on their reputation. Furthermore, we use trust management to dynamically adjust this reputation based on the honest or malicious behaviour of all connected peers. Using these techniques, a node is able to create links to other nodes and identify possible misbehaving friends. Finally, we present simulation results of a reputation based Turtle Friend-to-Friend network.

Evaluating transaction trust and risk levels in peer-to-peer e-commerce environments

Trust evaluation is critical to peer-to-peer (P2P) e-commerce environments. Traditionally the evaluation process is based on other peers' recommendations neglecting transaction amounts. This may lead to the bias in transaction trust evaluation and risk the new transaction. The weakness may be exploited by dishonest sellers to obtain good transaction reputation by selling cheap goods and then cheat buyers by selling expensive goods. In this paper we present a novel model for transaction trust evaluation, which differentiates transaction amounts when computing trust values. The trust evaluation is dependent on transaction history, the amounts of old transactions, and the amount of the new transaction. Therefore, the trust value can be taken as the risk indication of the forthcoming transaction and is valuable for the decision-making of buyers.     

Distributed recommender for peer-to-peer knowledge sharing

A novel model of distributed knowledge recommender system is proposed to facilitate knowledge sharing among collaborative team members. Different from traditional recommender systems in the client-server architecture, our model is oriented to the peer-to-peer (P2P) environment without the centralized control. Among the P2P network of collaborative team members, each peer is deployed with one distributed knowledge recommender, which can supply proper knowledge resources to peers who may need them. This paper investigates the key techniques for implementing the distributed knowledge recommender model. Moreover, a series of simulation-based experiments are conducted by using the data from a real-world collaborative team in an enterprise. The experimental results validate the efficiency of the proposed model. This research paves the way for developing platforms that can share and manage large-scale distributed knowledge resources. This study also provides a new framework for simulating and studying individual or organizational behaviors of knowledge sharing in a collaborative team.     

P2Care: A dynamic peer-to-peer network for collaboration in personalized healthcare service delivery

In the healthcare domain, the quality of personalized service delivery is strongly tied with collaboration among multiple stakeholders for accurate and reliable access to a variety of distributed and heterogeneous information and services. Existing healthcare information systems lack collaboration since they offer limited accessibility to resources and pose many restrictions in terms of scalability and flexibility. In this work, we propose P2Care, a decentralized, dynamic, scalable and self-organized network that exploits the functionalities of the structured peer-to-peer networks in order to provide collaboration. P2Care provides efficient ubiquitous access to medical information by organizing participating entities into groups according to common characteristics and interests. It incorporates enhanced lookup mechanisms with low response time and signaling overhead. The performance of the P2Care system has been evaluated through simulations. A prototype application demonstrates the effectiveness of the proposed system for cooperation and collaboration in order to facilitate personalized service delivery.     

Gossip-based aggregation of trust in decentralized reputation systems

Decentralized Reputation Systems have recently emerged as a prominent method of establishing trust among self-interested agents in online environments. A key issue is the efficient aggregation of data in the system; several approaches have been proposed, but they are plagued by major shortcomings. We put forward a novel, decentralized data management scheme grounded in gossip-based algorithms. Rumor mongering is known to possess algorithmic advantages, and indeed, our framework inherits many of their salient features: scalability, robustness, a global perspective, and simplicity. We demonstrate that our scheme motivates agents to maintain a very high reputation, by showing that the higher an agent’s reputation is above the threshold set by its peers, the more transactions it would be able to complete within a certain time unit. We analyze the relation between the amount by which an agent’s average reputation exceeds the threshold and the time required to close a deal. This analysis is carried out both theoretically, and empirically through a simulation system called GossipTrustSim. Finally, we show that our approach is inherently impervious to certain kinds of attacks. A preliminary version of this article appeared in the proceedings of IJCAI 2007.     

Evolvable agents,a fine grained approach for distributed evolutionary computing: walking towards the peer-to-peer computing frontiers

In this work we propose a fine grained approach with self-adaptive migration rate for distributed evolutionary computation. Our target is to gain some insights on the effects caused by communication when the algorithm scales. To this end, we consider a set of basic topologies in order to avoid the overlapping of algorithmic effects between communication and topological structures. We analyse the approach viability by comparing how solution quality and algorithm speed change when the number of processors increases and compare it with an Island model based implementation. A finer-grained approach implies a better chance of achieving a larger scalable system; such a feature is crucial concerning large-scale parallel architectures such as peer-to-peer systems. In order to check scalability, we perform a threefold experimental evaluation of this model: first, we concentrate on the algorithmic results when the problem scales up to eight nodes in comparison with how it does following the Island model. Second, we analyse the computing time speedup of the approach while scaling. Finally, we analyse the network performance with the proposed self-adaptive migration rate policy that depends on the link latency and bandwidth. With this experimental setup, our approach shows better scalability than the Island model and a equivalent robustness on the average of the three test functions under study.     

An efficient incentive scheme with a distributed authority infrastructure in peer-to-peer networks

Today’s peer-to-peer networks are designed based on the assumption that the participating nodes are cooperative, which does not hold in reality. Incentive mechanisms that promote cooperation must be introduced. However, the existing incentive schemes (using either reputation or virtual currency) suffer from various attacks based on false reports. Even worse, a colluding group of malicious nodes in a peer-to-peer network can manipulate the history information of its own members, and the damaging power increases dramatically with the group size. Such malicious nodes/collusions are difficult to detect, especially in a large network without a centralized authority. In this paper, we propose a new distributed incentive scheme, in which the amount that a node can benefit from the network is proportional to its contribution, malicious nodes can only attack others at the cost of their own interests, and a colluding group cannot gain advantage by cooperation regardless of its size. Consequently, the damaging power of colluding groups is strictly limited. The proposed scheme includes three major components: a distributed authority infrastructure, a key sharing protocol, and a contract verification protocol.     

P2Cast: peer-to-peer patching for video on demand service

Providing video on demand (VoD) service over the Internet in a scalable way is a challenging problem. In this paper, we propose P2Cast—an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key technical issues in P2Cast: (1) constructing an application overlay appropriate for streaming; and (2) providing continuous stream playback (without glitches) in the face of disruption from an early departing client. Our simulation experiments show that P2Cast can serve many more clients than traditional client-server unicast service, and that it generally out-performs multicast-based patching if clients can cache more than 10% of a stream’s initial portion. We handle disruptions by delaying the start of playback and applying the shifted forwarding technique. The threshold in P2Cast, i.e., the length of time during which arriving clients form a single session, can serve as a “knob” to adjust the balance between the scalability and the clients’ viewing quality.

P2MCMD: A scalable approach to VoD service over peer-to-peer networks

In this paper, we deal with the problem of providing Video-on-Demand service under Scheduled Video Delivery (SVD) paradigm, which allows users to request a video for instant viewing or later viewing. In this paradigm, a major concern for the server is how to schedule the delivery of requested videos to assure users a continuous playback while utilizing its limited channels more efficiently. We propose a novel approach, P2MCMD, to deal with this problem. P2MCMD treats the service of video delivery in finer granularity. What is scheduled is segment-based multicast groups instead of video-based multicast groups. With the aid of dynamic multicast, the server is enabled to allocate its delivery resources flexibly and efficiently. The uneven popularity of provided videos are also taken into account and exploited so that the produced delivery schedule has the potential to boost the degree of multicast, thereby making the server achieve higher channel utilization. Moreover, peer-to-peer cooperation is employed to alleviate the workload at the server. The server can be relieved of the retransmission of duplicate video segments by enabling a latecomer to appeal to early arriving peers for the missing initial video segments. The performance of P2MCMD is evaluated through simulation. The experimental results show that our proposed approach can scale to a larger number of requests and allow the provision of a wider variety of videos at the server as compared to the existing approaches for SVD paradigm.