1. Stacey Greenaway Managing Reputation and Trust in Peer-to-Peer Networks. CP4022 Research Topics in Networks and Distributed Systems. Assessment 2 Stacey Greenaway 0487622

2. Introduction Definitions Research 1 - XRep Research 2 – Bayesian Network Research 3 - Trust Vectors Research 4 – EigenTrust Problems Conclusions Introduction Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

3. What is a peer–to-peer network? A decentralized network All nodes in the network act as both clients and servers Powered by the bandwidth of all peers Ad hoc connections Types of P2P Network: Filesharing e.g. Gnutella, Kazaa, BitTorrent Instant Messaging, Ecommerce Exisitng Trust and Reputation systems: Ebay Feedback System Introduction Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

4. Trust - A peer’s trust in other peers based on his own past experience. Reputation - A peer’s trust in another peer based on the experiences of other peers. File Provider – a peer providing a file for download Servent – 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 be. Definitions Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

5. These 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 with 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 – 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 Threats Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

6. A 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 another 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 Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

7. A Reputation Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks (Damiani et al.) [1] Cited by the other 3 research papers. [2] [3] [4] Propose a protocol called XRep: A peer, p, queries the network for other peer’s opinions (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 (-) = 0. Research 1 - XRep Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

8. A Reputation Based Approach for Choosing Reliable Resources in Peer-to-Peer Networks (Damiani et al.) [1] XRep has six phases: Resource Searching and Resource Selection: query network, retrieving list of files, 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 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 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 1 - XRep Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

9. Trust 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 peer’s opinions based on different contexts of trust. An Analogy: Mike has two friends John who is a mechanic and Bob who is a Doctor. Mike trusts 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. What one peer may consider a good file is not what another peer would consider good. For instance peer A’s priority in a good file is its content regardless of its quality. Research 2 - Bayesian Network Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

10. Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2] Bayesian network - A Bayesian Network is a graph consisting of nodes and arcs. Nodes represent variables and the arcs represent the relationships and dependencies between the variables. ([5]) Fig 1 - basic Bayesian network consisting only of one parent and several child nodes. FP - file provider T - % of all positive interactions. Trust is dependant on Download Speed (DS), File Quality (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 Network Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 1 – Naïve Bayesian Network

11. Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2] The Trust and Reputation Model: A peers reliability is measured as a file provider and a referee. Queries are issued about the reliability of a file and its provider. A Bayesian Network contains a peers opinions on all past interactions Bayesian 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 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 will be obvious when compared to trusted peers and counteracted) Research 2 - Bayesian Network Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

12. Trust and Reputation Model in Peer-to-Peer Networks (Wang et al.) [2] Experiments: Simulated a file sharing system based on Gnutella. Each node is either a file provider or peer at one time. Each peer only knows its direct neighbour and a few file providers. 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 BN Trust and Reputation without BN Trust with BN Trust without BN results: Systems where opinions are exchanged perform better. Using a BN only gives marginal increase in performance. Research 2 - Bayesian Network Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

13. A Reputation-Based Trust Management System 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 peers Consult own “trust vector”, or request a “trust rating” from other peers Uses query messages to gain recommendations from other peers “Trust Vectors” are updated after every download with + or – opinion. Research 3 - Trust Vectors Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

14. A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3] Trust Vectors are binary consist of 8, 16, or 32 bits length is stored as an integer variable positive (1) or negative (0) opinion is represented in the vector as 1 bit updates 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 the power of the number of significant bits in the vector. Distrust Rating Has more weight than a Trust Rating Malicious action hard to recover from Research 3 - Trust Vectors Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 2 Trust Vector Fig 3 Trust Rating

15. A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3] The trust ratings of file providers are evaluated: an average of the trust values of the most trusted peers in the list is calculated determined by a set threshold value. If No. Trusted Peers < Threshold a random selection of peers are queried trust and distrust ratings will be included in the responses. credibility rating gives weight to the opinions. credibility vector - peer’s opinion truthful (1) untruthful (0) Threshold sets number of responses to evaluate. Research 3 - Trust Vectors Managing Reputation and Trust in Peer- to- Peer Networks. Stacey Greenaway Fig 3 Trust Evaluation

16. A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3] Experiments: Test performance under various malicious attacks: naïve hypocritical malicious collective pseudospoofing Simulation Spec: 1000 peers and 1000 files between 1% and 10% malicious peer linked to 3 neighbours, query submitted over these links for 3 hops, specified by TTL Research 3 - Trust Vectors Managing Reputation and Trust in Peer- to- Peer Networks. Stacey Greenaway

17. Research 3 - Trust Vectors Managing Reputation and Trust in Peer- to- Peer Networks. Stacey Greenaway   - represent’s the ratio of malicious to all downloads. Fig 4 Results A Reputation-Based Trust Management System for P2P Networks (Selcuk et al.) [3]

18. The 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 assigns 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 rather than the inauthentic files Decoy Files Self Replicating Worms Malicious Collectives Malicious Spies Pseudospoofing Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

19. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Basic Trust 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 i’s experiences downloading from other peer’s, j. Global Trust Value - calculated from the local trust values assigned to peer i by 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 their 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 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

20. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Basic Trust Model Pre Trusted Peers, P Peers who established the system or first users Their distribution across the network = Their trust values are used to break up malicious collectives or when a referee is inactive. Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 5 Basic EigenTrust Algorithm

21. The 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 system Assigned using Distributed Hash Table (DHT) Set of Daughter Peers, D i Distributed Hash Table hash 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 that ID is hashed becomes that peers score manager. Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 6 Can Hash Space (cited by Kamvar et al. (13) [4])

22. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Secure Trust Model Score Managers - computing Global Trust computes the Global Trust Values of its Daughters holds an opinion vector queried to find the trust worthiness of d, where The score manager learns: set of peers who download from d and their opinion of d set of peers who d has downloaded from and its opinion of those interactions. Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway

23. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Secure EigenTrust Algorithm Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 7 Secure EigenTrust Algorithm

24. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Experiments Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 8 Simulation Settings

25. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Experiments Threat Model A individual malicious peers Threat Model B malicious collectives Amount 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% in a network without the proposed trust model. Using values of Pre Trusted Peers has broken up the malicious collective. Research 4 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 9 Threat Model A Fig 10 Threat Model B

26. The EigenTrust Algorithm for Reputation Management in P2P Networks (Kamvar et al.) [4] Experiments Threat Model C malicious 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 uploaded 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 - EigenTrust Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 11 Threat Model C

27. Performance Bottleneck Main problem facing Trust and Reputation systems Peer with highest reputation always selected as download source No suitable solution proposed Random selection Priority queue Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Fig 12 Performance Bottleneck [4] Problems

28. None of the proposed systems have been implemented in ‘real world’ Hard to measure trust and reputation when measuring human opinion No standards that define what trust and reputation is and how it should be measured Policing Quality of Service in P2P networks is virtually impossible due to their decentralized nature. Each individual peer is responsible for the quality of the content they provide only Not suitable for large P2P networks, only tested on small simulations. Performance of these systems not tested in terms of bandwidth. Excessive messaging places too much strain on network. Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway Conclusions

29. [1] Damiani, E. di Vimercati, D. C. Paraboschi, S. Samarati, P. Violante, F. (2002) Reputation-based approach for choosing reliable resources in peer-to-peer networks, Proceedings of the 9th ACM Conference on Computer and Communications Security. [2] Wang, Y. Vassileva, J. (2003) Trust and Reputation Model in Peer-to-Peer Networks, Proceedings of IEEE Conference on P2P Computing, Linkoeping, Sweden. [3] A. A. Selcuk, E. Uzun, M. R. Pariente (2004), A Reputation-Based Trust Management System for P2P Networks, 4th IEEE/ACM International Symposium on Cluster Computing and the Grid (CCGrid 2004), Chicago, USA. [4] Kamvar, S. D. Schlosser, M. T. and Garcia-Molina. H. (2003) The eigentrust algorithm for reputation management in P2P networks, Proceedings of the Twelfth International World Wide Web Conference. [5] Wikipedia, the free encyclopedia (no date) Bayesian Network [online]. [cited 14th Apr 2006]. http://en.wikipedia.org/wiki/Bayesian_Network [6] Wikipedia, the free encyclopedia (no date) Eigenvector [online]. [cited 14th Apr 2006]. http://en.wikipedia.org/wiki/Eigenvector [7] Eric W. Weisstein. "Eigenvector." (no date) MathWorld--A Wolfram Web Resource. [cited 14th Apr 2006]. Managing Reputation and Trust in Peer-to-Peer Networks. Stacey Greenaway References