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Design and Implementation of a Reliable Reputation System for File Sharing in P2P Networks

Design and Implementation of a Reliable Reputation System for File Sharing in P2P Networks. 2006/7/6 黃盈傑. Outline. Introduction Related Work System Overview Experimental Results Demo Conclusions & Future Works. Introduction. Problem.

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Design and Implementation of a Reliable Reputation System for File Sharing in P2P Networks

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  1. Design and Implementation of a Reliable Reputation System for File Sharing in P2P Networks 2006/7/6黃盈傑

  2. Outline • Introduction • Related Work • System Overview • Experimental Results • Demo • Conclusions & Future Works

  3. Introduction Problem Because of the anonymity of P2P, there is a problem that some of file providers may misuse by providing tampered files.

  4. Introduction Reputation System It is hard for a user himself to gather enough information to get a trust value about other users directly.

  5. Introduction Attacks to P2P Network • Distribution of tampered with information • Man in the middle attack V U M Intercept & Modify

  6. Introduction Attacks to Reputation System(1/2) • Reentry to get rid of the bad history • Self replication

  7. Introduction Attacks to Reputation System(2/2) • Pseudospoofing • Shilling attacks

  8. Introduction Motivation • Defend the attacks. • Design a mechanism to determine the judgments are real or not.

  9. Outline • Introduction • Related Work • System Overview • Experimental Results • Demo • Conclusions & Future Works

  10. Related Work Recommendation-based P2P trust model • “A recommendation-based peer-to-peer trust model” [DWJZ 2004] • Pure P2P • Any node x has a corresponding file node Dx that stores all information for it.

  11. Related Work Calculation Formula Rij: node i’s recommend degree on node j Sij: successful transactions from node j to node i. Fij: unsuccessful transactions from node j to node i. Trust Calculation formula

  12. u download v Suv orFuv echo Dv Related Work Restrain Slander & Magnify • For every transaction, when node u puts evaluation, node v also needs to echo it in a period of time. • If node u puts evaluation too frequently, the evaluation may not be accepted.

  13. Related Work Restrain Slander • When node uputs Fuv(negative evaluation) • If node v echos in time: Fuv is accepted. • If node v does not echo in time: Fuv is accepted in the probability of 1-Tv Node v is harder to be slandered when he has a higher trust value.

  14. Related Work Restrain Magnify • When node u puts Suv(positive evaluation) • If node v echos in time: Suv is accepted in the probability of Tv. • If node v does not echo in time: Suv is not accepted. Node v is harder to be magnified when he has a lower trust value.

  15. Outline • Introduction • Related Work • System Overview • Experimental Results • Demo • Conclusions & Future Works

  16. System Overview Formulas Design (1/4) Global Trust Calculate Formula: Cx: client x GTx: Cx’s Global Trust -1 GTx 1, w = 2 gx = Counts of “Good” judgments of Cx bx = Counts of “Bad” judgments of Cx vbx = Counts of “Very bad” judgments of Cx Jx = gx + bx +w* vbx

  17. System Overview Formulas Design (2/4) Self-Trust Calculate Formula: STxy: Self-trust for Cx to Cy -1 STxy 1, w = 2 gxy = Counts of “Good” judgments that Cx report to Cy bxy = Counts of “Bad” judgments that Cx report to Cy vbxy = Counts of “Very bad” judgments that Cx report to Cy Jxy = gxy + bxy + w*vbxy

  18. System Overview Formulas Design (3/4) Reputation Calculate Formula: REPxyrepresents Cy’s reputation to Cx. -1 REPxy 1, 0  1

  19. System Overview Formulas Design (4/4) Modification Reputation Calculate Formula: -1 REPxy 1, 0  1, w =2 z: all the other clients in the P2P network

  20. System Overview System Framework of On-line Server

  21. System Overview System Framework of Off-line Server

  22. System Overview Database: reputation_data table

  23. System Overview Database: client_registration table

  24. System Overview Report Record File(1/2)

  25. System Overview Report Record File(2/2) ex: RP#true#2006/07/02 14:23:37 – 1151821417218 #uuid-59616261646162614A7874615032503386 C56C2FCE0C42CA9031D5989E73FD4F03 #md5:6604305f08ca5b498b5596cbaf901acb #72_cs 3-3.txt#A:Good file

  26. System Overview Defend the Attacks(1/2) • Distribution of tampered with information Use the reputation as the guide to select file provider. • Reentry to get rid of the bad history ID-Design: mac_address + JXTA-ID

  27. System Overview Defend the Attacks(2/2) • Self replication Our reputation system not voting mechanism. Client can't download the files offered by themselves • Pseudospoofing ID-Design: mac_address + JXTA-ID • Shilling attacks Use the Monitor to detect to probably malicious attack.

  28. System Overview Concept of the Monitor • System will accept all reports from the clients. • Set the alert threshold (parameter) of each action. • The Monitor acts according those parameters.

  29. System Overview Monitor Flowchart Check all client’s report record file and set their alert-state with corresponding parameters Determine F9,F12~F16 Any client’s alert-state is set? Monitor Start Check Phase Determine Phase Yes Client was restrained from reporting while F-8~F-12 is set No Monitor Done

  30. System Overview Determine Phase • Cx: the client whose alert-state is set • Cg: the client gives Cx good judgment • Cb: the client gives Cx bad judgment • GTx: global trust of Cx, -1 GTx 1 • NGTx: normalize global trust of Cx, 0 NGTx 1

  31. System Overview Determine Phase: F-9 Cg gives too many good judgments to a specific file of Cx • Determine: Cg magnifies Cx • Punish Cg • Remove those good judgments from Cx

  32. System Overview Determine Phase:F-12 Cb gives too many bad judgments to a specific file of Cx • Determine: Cb slanders Cx • Punish Cb • Remove those bad judgments from Cx

  33. System Overview Determine Phase: F-14 Cx gets too many good judgments from Cg Do nothing Yes Are there other judgments? P(NGTx) accept? No Reset F-14 of Cx Remove those good judgments from Cx No Yes Do nothing

  34. System Overview Determine Phase: F-16 Cx gets too many bad judgments from Cb P(1-NGTx) accept? Do nothing Yes Yes Are there other judgments? P(NGTb) accept? No No Reset F-16 of Cx Remove those bad judgments from Cx No Yes Do nothing

  35. System Overview Determine Phase: F-13 Cx gets too many good judgments Do nothing Yes final_judge > 0? Calculate avg_good_judgment, avg_bad_judgment & final_judge Reset F-13 of Cx Remove those good judgments from Cx, Punish Cg No avg_good_judgment = mean(∑(NGTg)) avg_bad_judgment = mean(∑(NGTb)) final_judge = avg_good_judgment - avg_bad_judgment

  36. System Overview Determine Phase: F-15 Remove those bad judgments from Cx, Punish Cb Cx gets too many bad judgments Yes final_judge > 0? Calculate avg_good_judgment, avg_bad_judgment & final_judge Reset F-15 of Cx Punish Cx No avg_good_judgment = mean(∑(NGTg)) avg_bad_judgment = mean(∑(NGTb)) final_judge = avg_good_judgment - avg_bad_judgment

  37. Outline • Introduction • Related Work • System Overview • Experimental Results • Demo • Conclusions & Future Works

  38. Experimental Results Experiment 1 (1/3) • 1,000 clients, 10,000 files. • Each client downloads 100 files. • This is an ideal network, so that any client can find all files of all clients, and he selects the file owner who has the highest reputation to be the file provider. • A successful downloading means the client gets the right file he wants.

  39. Experimental Results Experiment 1 (2/3) Types of bad clients: • Type-1:Bad provider. A bad client provides wrong files only. • Type-2:Slanderer. Give a “bad” judgment to the client who provides him the right file. • Type-3:Magnifier. Give a “good” judgment to the client who even provides him the wrong file.

  40. Experimental Results Experiment 1 (3/3) • Client Setting: •  = 1,  = 1. • Server Monitor Setting: • Acts after every 5,000 downloadings. • Determine F-13 (Cx gets too many good judgments) & F-15 (Cx gets too many bad judgments) • Both thresholds are set as 5.

  41. Experimental Results Exp1 Type-1:Bad provider

  42. Experimental Results Exp1 Type-2:Slanderer

  43. Experimental Results Exp1 Type-3:Magnifier

  44. Experimental Results Experiment 2 Compare different variations of on-line clients.

  45. Experimental Results Exp2 Type-1:Bad provider

  46. Experimental Results Exp2 Type-2:Slanderer

  47. Experimental Results Exp2 Type-3:Magnifier

  48. Experimental Results Experiment 3 Compare “clients select file providers highest reputation” and “clients select the file owner who has the reputation higher than a threshold”

  49. Experimental Results Exp3 Type-1:Bad provider

  50. Experimental Results Exp3 Type-2:Slanderer

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