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1、 Stacey GreenawayManaging Reputation and Trust in Peer-to-Peer Networks. CP4022 Research Topics in Networks and Distributed Systems. Assessment 2Stacey Greenaway 0487622IntroductionDefinitionsResearch 1 - XRepResearch 2 Bayesian NetworkResearch 3 - Trust Vectors Research 4 EigenTrustProblemsConclusi

2、onsIntroductionManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayWhat is a peerto-peer network?A decentralized networkAll nodes in the network act as both clients and serversPowered by the bandwidth of all peersAd hoc connectionsTypes of P2P Network:Filesharing e.g. Gnutella, K

3、azaa, BitTorrentInstant Messaging, EcommerceExisitng Trust and Reputation systems:Ebay Feedback SystemIntroductionManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayTrust - A peers trust in other peers based on his own past experience.Reputation - A peers trust in another peer b

4、ased on the experiences of other peers.File Provider a peer providing a file for downloadServent a peer who is both client and server.Free Rider - A peer who only downloads and does not share any files. Inauthentic files viruses, corrupt, unreadable, wrong file type, content not what it claimed to b

5、e.DefinitionsManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayThese threats provide the requirements for a trust and reputation system.Decoy files - A malicious peer will respond to any query with a copy of the requested file, but will deliver a file that has been tampered wit

6、h or contains a virus at the point of download. Malicious peer - A peer who either belongs to one of the groups below or will provide an inauthentic file for every request. Malicious collective - A group of malicious peers who know each other and collaborate to subvert a P2P system.Self Replication

7、virus such as Gnutella vbs.worm poses as a peer and then creates a copy of itself for download.Pseudospoofing - malicious peers control multiple identities, false pseudonyms are used to give good reputation to other pseudonyms controlled by the same malicious peer.Definitions Attacks and ThreatsMana

8、ging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayA basic explanation of a Trust and Reputation system:Peers store opinions on their experiences at downloading files.They store an opinion about the file provider and the file.These opinions are computed either into binary or using a

9、nother mathematical probability.Peers share their opinions providing recommendations for file providers and files.A peers opinion can be weighted based on how much the querying peer trusts them.The aim of the system is to eliminate malicious peers and inauthentic files. A Trust and Reputation System

10、Managing Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayA Reputation Based Approach for Choosing Reliable Resources inPeer-to-Peer Networks (Damiani et al.) 1Cited by the other 3 research papers. 2 3 4Propose a protocol called XRep:A peer, p, queries the network for other peers opini

11、ons (votes) on resources and servents.Resource repository - records an ID for each file downloaded and whether it is good(+) or bad(-) Servent repository - stores the number of successful and unsuccessful downloads by each peer. Votes are converted to binary, where a positive (+) = 1 and negative (-

12、) = 0. Research 1 - XRepManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayA Reputation Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks (Damiani et al.) 1XRep has six phases: Resource Searching and Resource Selection:query network, retrieving list of file

13、s, select one to download based on trust and reputation. Vote Polling:peer (p) asks the other peers opinions (poll request) about the resource (r) it is about to download or on the servent (s) offering the resource. Poll responses encrypted using a public key called “pkpoll - contains the responding

14、 peers vote, IP Address and Port. Vote Evaluation:“pkpoll decrypted. p clusters the votes, which allows it to detect those sharing the same IP address. (pseudospoofing)An average value of all votes in the cluster is calculated and returned to the querying peer (p). A random selection of “voters from

15、 each cluster is contacted for confirmation of their vote using the IP and Port encypted in “pkpoll. Best Servent Check:Choose the most reputable servent to download file from. Resource Downloading:After download, p updates his repositories with his opinion of both the servent and resource. Research

16、 1 - XRepManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayTrust and Reputation Model in Peer-to-Peer Networks (Wang et al.) 2 Propose a Trust and Reputation Model using Bayesian Networks to build a profile of each peers opinions based on different contexts of trust.An Analogy:

17、Mike has two friends John who is a mechanic and Bob who is a Doctor. Miketrusts Bob with a medical complaint but not to fix his car and respectively, trusts John to fix his car but not to diagnose a medical condition. So in the context of fixing a car John is trustworthy, but Bob is untrustworthy. W

18、hat one peer may consider a good file is not what another peer would considergood. For instance peer As priority in a good file is its content regardless of its quality. Research 2 - Bayesian NetworkManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayTrust and Reputation Model in

19、 Peer-to-Peer Networks (Wang et al.) 2Bayesian network - A Bayesian Network is a graph consistingof nodes and arcs. Nodes represent variables and the arcs represent the relationships and dependencies between thevariables. (5)Fig 1 - basic Bayesian network consisting only of one parent and several ch

20、ild nodes. FP - file providerT - % of all positive interactions. Trust is dependant on Download Speed (DS), FileQuality (FQ) and File Type (FT). More user preferences can be added to the Bayesian Network, e.g. copyright.Trust in a condition only calculated once.Research 2 - Bayesian NetworkManaging

21、Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 1 Nave Bayesian NetworkTrust and Reputation Model in Peer-to-Peer Networks (Wang et al.) 2The Trust and Reputation Model:A peers reliability is measured as a file provider and a referee.Queries are issued about the reliability of a f

22、ile and its provider.A Bayesian Network contains a peers opinions on all past interactionsBayesian Networks are exchanged and compared to form groups of trusted peers. (The similarity of nodes is calculated, nodes with higher value (between 0 and 1) indicate peer preferences) The recommendations of

23、trusted peers are weighted more heavily than unknown peers as they share similar preferences.After every interaction the BNs of the file provider and the referees will be updated to reflect the peers trust in them.Combat pseudospoofing or malicious collective attacks. (false recommendation values wi

24、ll be obvious when compared to trusted peers and counteracted)Research 2 - Bayesian NetworkManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayTrust and Reputation Model in Peer-to-Peer Networks (Wang et al.) 2Experiments:Simulated a file sharing system based on Gnutella.Each nod

25、e is either a file provider or peer at one time.Each peer only knows its direct neighbour and a few fileproviders.1000 interactions between 40 peers and 10 file providers.Each of ten runs is evaluated by taking averages of the results.4 systems compared:Trust and Reputation with BNTrust and Reputati

26、on without BNTrust with BN Trust without BNresults: Systems where opinions are exchanged perform better.Using a BN only gives marginal increase in performance.Research 2 - Bayesian NetworkManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayA Reputation-Based Trust Management Syst

27、em for P2P Networks (Selcuk et al.) 3 Propose a protocol to control the amount of inauthentic files a malicious peer can disperse through the P2P network.“Trust Vectors are kept locally by peersConsult own “trust vector, or request a “trust rating from other peersUses query messages to gain recommen

28、dations from other peers“Trust Vectors are updated after every download with + or opinion. Research 3 - Trust VectorsManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayA Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) 3 Trust Vectorsare binary consist o

29、f 8, 16, or 32 bitslength is stored as an integer variablepositive (1) or negative (0) opinion is represented in the vector as 1 bitupdates recorded at the vectors most significant bit Trust Rating Calculated by dividing the sum of the Trust Vector by the power of 2, then dividing the result by 2 to

30、 the power of the number of significant bits in the vector.Distrust RatingHas more weight than a Trust RatingMalicious action hard to recover from Research 3 - Trust VectorsManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 2 Trust Vector Fig 3 Trust RatingA Reputation-Based

31、 Trust Management System for P2P Networks (Selcuk et al.) 3The trust ratings of file providers are evaluated:an average of the trust values of the most trusted peers in the list is calculateddetermined by a set threshold value.If No. Trusted Peers Threshold a random selection of peers are queriedtru

32、st and distrust ratings will be included in the responses.credibility rating gives weight to the opinions.credibility vector - peers opinion truthful (1) untruthful (0)Threshold sets number of responses to evaluate. Research 3 - Trust VectorsManaging Reputation and Trust in Peer- to- Peer Networks.

33、Stacey GreenawayFig 3 Trust EvaluationA Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) 3Experiments:Test performance under various malicious attacks:navehypocriticalmalicious collectivepseudospoofingSimulation Spec:1000 peers and 1000 files between 1% and 10% maliciouspeer

34、 linked to 3 neighbours, query submitted over these links for 3 hops, specified by TTLResearch 3 - Trust VectorsManaging Reputation and Trust in Peer- to- Peer Networks. Stacey GreenawayResearch 3 - Trust VectorsManaging Reputation and Trust in Peer- to- Peer Networks. Stacey GreenawayF1 - represent

35、s the ratio of malicious to all downloads. Fig 4 ResultsA Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) 3The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 “to decrease the number of inauthentic files in a P2P file sharing system that ass

36、igns each peer a unique global trust value based on the peers history of uploads. 5 Eigenvectors A special set of vectors associated with Linear Algebra, and matrixes, where left eigenvector is a row of the matrix and right eigenvector is a column of the matrix. (6 7)Malicious peers identified rathe

37、r than the inauthentic files Decoy FilesSelf Replicating WormsMalicious CollectivesMalicious SpiesPseudospoofingResearch 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayThe EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 Basic Tr

38、ust Model “Peer i is more likely to trust the opinions of peers from whom he has had an honest interaction with in the past. . 5 Local Trust Value - calculated from peer is experiences downloading from other peers, j. Global Trust Value - calculated from the local trust values assigned to peer i by

39、peers j. based on their experiences downloading from i.Each peer computes its own Global Trust Value and stores it locally.Local Trust Values are normalized.Peers share Trust Values.Trust Values are weighted by the amount of trust peer i places in the referee.Peer i asks the friends of friends for t

40、heir recommendations until a view of entire network is achieved.Eventually Global Trust Value will represent the trust the entire system holds in peer j. Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayThe EigenTrust Algorithm for Reputation Management

41、in P2P Networks (Kamvar et al.) 4 Basic Trust Model Pre Trusted Peers, PPeers who established the system or first usersTheir distribution across the network =Their trust values are used to break up malicious collectives or when a referee is inactive. Research 4 - EigenTrustManaging Reputation and Tr

42、ust in Peer-to-Peer Networks. Stacey GreenawayFig 5 Basic EigenTrust AlgorithmThe EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 Secure Trust Model Score Managers peers who compute the trust value of another peer in the systemAssigned using Distributed Hash Table (D

43、HT) Set of Daughter Peers, DiDistributed Hash Tablehash functions map a Unique ID for each peer (IP Address and TCP port) into points in a logical coordinate space.coordinate space is partitioned over the network, every peer covers a region of that dynamic space.the peer who covers the region where

44、that ID is hashed becomes that peers score manager. Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 6 Can Hash Space(cited by Kamvar et al. (13) 4)The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 Secure Trust Mode

45、l Score Managers - computing Global Trustcomputes the Global Trust Values of its Daughtersholds an opinion vectorqueried to find the trust worthiness of d, whereThe score manager learns:set of peers who download from d and their opinion of dset of peers who d has downloaded from and its opinion of t

46、hose interactions. Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayThe EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 Secure EigenTrust Algorithm Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Netw

47、orks. Stacey GreenawayFig 7 Secure EigenTrust AlgorithmThe EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) 4 Experiments Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 8 Simulation SettingsThe EigenTrust Algorithm for

48、 Reputation Management in P2P Networks (Kamvar et al.) 4 ExperimentsThreat Model Aindividual malicious peersThreat Model Bmalicious collectivesAmount of malicious peers is increased by 10% - max. 70%. Results:Inauthentic files make up approximately 10% of the network compared to a maximum of over 90

49、% in a network without the proposed trust model. Using values of Pre Trusted Peers has broken up the malicious collective.Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 9 Threat Model AFig 10 Threat Model BThe EigenTrust Algorithm for Reputation Ma

50、nagement in P2P Networks (Kamvar et al.) 4 ExperimentsThreat Model Cmalicious collectives with camouflage malicious peers who will offer authentic files some of the time in order to gain higher trust values, to increase their chance at being selected as a download source. Amount of authentic files u

51、ploaded is increased by 10% - max. 90%. Results:The more authentic files a malicious peer provides, the more impact they have at providing inauthentic files.Too costly in terms of bandwidth and disk space.Research 4 - EigenTrustManaging Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

52、Fig 11 Threat Model CPerformance BottleneckMain problem facing Trust and Reputation systemsPeer with highest reputation always selected as download sourceNo suitable solution proposedRandom selectionPriority queue Managing Reputation and Trust in Peer-to-Peer Networks. Stacey GreenawayFig 12 Performance Bottleneck 4ProblemsNone of the proposed systems have been implemented in real worldHard to measure trust an