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Detecting Reputation Variations in P2P Networks

Detecting Reputation Variations in P2P Networks. Theodora Dariotaki & Alex Delis Deprt. of Informatics & Telecommunications The University of Athens (th.dariotaki, ad)@di.uoa.gr. Basic Questions. How do reputation schemes work?. C. A. B. Why we might want to detect reputation variations?.

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Detecting Reputation Variations in P2P Networks

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  1. Detecting Reputation Variations in P2P Networks Theodora Dariotaki & Alex Delis Deprt. of Informatics & Telecommunications The University of Athens (th.dariotaki, ad)@di.uoa.gr The University of Athens

  2. Basic Questions • How do reputation schemes work? C A B • Why we might want to detect reputation variations? The University of Athens

  3. Reputation Monitoring Mechanism (RMM) • Monitors the reputation variations of offerers • Limits abrupt changes of reputation values • New concepts • RVM peers (Reputation Variation Monitor) • Epoch • Storage Structures: • DPE: DirectPeerExperience Table • DRE: DirectResourceExperience Table • RT: Reputation Table (RVM Only) The University of Athens

  4. Phase I - Resource Request Q: Where is the resource located? • Requester q dispatches an AskResource message asking for resource s • Offerers reply with a HoldResource message - Dispatch: The message is forwarded in a scope of h hops from q or until answered by a resource holder - Cycles: Messages received more than once, are discarded The University of Athens

  5. Phase II - Recommendation Request Q: Are the offerers trustworthy? • Requester q dispatches an AskRecom message for all offerers’ reputation and resource requested. • First-Line (FL) recommenders respond with PostRecom messages. - Prevention of blacklisting The University of Athens

  6. Phase III - Evaluation of Offerer/Resource Reputation Q: Are FL-recommenders reputable? • If Direct Peer Experience > θrecommendation accepted • If qhas never communicated with a FL-recommender, qdispatches an AskRecom message for FL’s reputation.Second-Line (SL) recommenders respond with PostRecom Q: Are SL-recommenders reputable? • Only if qhas direct experience with the SL-recommender and Direct Peer Experience > θthe recommendation is accepted q may rely - on its own opinion - on the recommenders’ opinion - on both The University of Athens

  7. Phase IV – Offerer SelectionBaseline Reputation Scheme (BRS) A peer is candidate for resource downloading if both: reputation level of the resource holder reputation level of the hosted resource exceed a threshold θ. • Candidate peers are sorted in a list • Random selection of one-of-top reputable peers to prevent overloading of most reputable peers • Challenge-response handshake between requester and resource offerer to ensure resource possession • Download initiation The University of Athens

  8. Phase IV – Offerer Selection (1/4)RMM Scheme • Step a: Early Offerer Selection • Offers with Peer/Resource reputation level < θ are discarded • Step b: Reputation Variation Request • Requester qdispatches anonymous AskRVMmessages • RVMs respond with RVMReplymessages - reputation levels of offerers during last λ epochs The University of Athens

  9. y x Phase IV – Offerer Selection (2/4) Step c: Evaluation of Offerer Reputation Variation • Relative Reputation Variation (V) is computed for all offerersas the fraction x/y x: difference between a previous and the last observed reputation level (0.67-0.84=-0.17) y: difference between the perfect reputation and the lowest of the two reputation levels (1.00-0.67=0.33) The University of Athens

  10. Step d: Reputation Update RRMM=0.75 RRMM=0.79 y y' x' x Dependence on 2 epochs General case: λ epochs Phase IV – Offerer Selection (3/4) • Requester q re-evaluates offerers’ reputation Dependence on 1 epoch The University of Athens

  11. Step e: Final Offerer Selection Phase IV – Offerer Selection (4/4) • Candidate peers are sorted in a list • Random selection of one-of-top reputable peers • Challenge-response handshake between requester and resource offerer • Download initiation The University of Athens

  12. Phase V – Resource Download & Experience Updates • The requester qasks for resource s from the selected offerer pw by sending a DownloadReq message • pwsends the resource • qrecords its satisfaction in both DPE & DREtables -DPE: satisfaction concerning selected offerer pw FL-recommenders for pw SL-recommenders for every FL-recommender of pw -DRE: satisfaction concerning downloaded resource The University of Athens

  13. Forwarding continues until max hops h are exceeded or the resource is found Resource holders reply with HoldResource Messages received twice are discarded Peers forward the query Requester broadcasts an AskResource (h=3) Example 13 4 12 found 3 5 10 11 1 2 7 6 AskResource HoldResource 8 9 found h = 3 1: requester 8&10: resource holders The University of Athens

  14. 11 replies for 8 with PostRecom 6 & 13reply for 10 11 & 13are unknown to requester 6 has been proven trustworthy Requester broadcasts AskRecom for both 8 & 10 and resource s Example found 13 4 12 3 5 10 11 1 2 found 7 6 found AskRecom PostRecom 8 9 1: requester 8&10: resource holders The University of Athens

  15. 12 replies for 11 with PostRecom 9replies for 13(but 9 is unknown to 1) Requester dispatches an AskRecom for 11 & 13 Assume that 12 claims that 11 is trustworthy. Then 11’s recomme-ndation for 8 is accepted. Example 13 4 found 12 3 5 10 11 1 2 7 6 AskRecom PostRecom 8 9 found 1: requester 8&10: resource holders The University of Athens

  16. Requester considers 8 to be more reputable than 10 and downloads the resource from 8 (BRS) Example 13 4 12 3 5 10 11 1 2 7 6 DownloadReq 8 9 1: requester 8&10: resource holders The University of Athens

  17. RVMs respond with RVMReply sending the reputation values for 8&10 during previous λ epochs Requester sends anonymous AskRVM asking for reputation variations on 8&10 Requester computes the Relative Variation Values for 8&10 and detects abrupt changes in 8’s reputation Example(RMM) 13 4 12 3 5 10 11 1 2 7 6 AskRVM RVMReply 14 8 9 15 16 RVM peers 1: requester 8&10: resource holders The University of Athens

  18. Requester downloads the resource from 10 Example 13 4 12 3 5 10 11 1 2 7 6 DownloadReq 8 9 1: requester 8&10: resource holders The University of Athens

  19. BRS vs. RMM λ = 3 The University of Athens

  20. Discussion (1/3) • Pseudospoofing & Shilling Attacks • Smooth out abrupt changes • Challenge-response handshake • Bind with real-world identities • Man-in-the-middle • Message Authentication/Integrity Check • RVM • anonymity • impersonation • failure The University of Athens

  21. Discussion (2/3) • Number and Duration of Epochs • Average frequency fx of download requests in popular peers • Network population N • Space Overhead of RVM • λxNp(Np:average # of peers assigned to a RVM) The University of Athens

  22. Discussion (3/3) • Communication Cost • (k: average # of neighboring nodes, h: max # of hops) • More efficient solutions: • Select kr most reputable neighbors • Use a P2P routing protocol (e.g. Chord with O(logN) messages, N: network population) • Anonymity cost for RVMs (e.g. Tarzan with O(N) messages) The University of Athens

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