CPSC 426/526. Reputation Systems. Ennan Zhai. Computer Science Department Yale University

CPSC 426/526 Reputation Systems Ennan Zhai Computer Science Department Yale University

Recall: Lec-4 P2P search models: - How Chord works - Provable guarantees in Chord - Other DHTs, e.g., CAN and Pastry - Comparison between structured and unstructured P2P models red P2P network (Gnutella, KaZaA, etc.)tructured P2P network (Chord, CAN, Pastry, etc.) - More (Hybrid P2P, BitTorrent, etc.)

Lecture Roadmap Background Reputation Systems Case Study: Credence

Background foo A

Background NO Name Ava/Source 1 Foo1 70/78 2 foo1 60/66 foo A 3 foo 11/40 4 Foo-4 2/3 5......

Background NO Name Ava/Source Polluted { 1 Foo1 70/78 2 foo1 60/66 3 foo 11/40 4 Foo-4 2/3 5......

Pollution Attacks There are many ways to pollute files: - Corrupting the majority content of the files - Corrupting the block downloading at 99%

Pollution Attacks There are many ways to pollute files: - Corrupting the majority content of the files - Corrupting the block downloading at 99% I spent 3 hours on downloading this file, but this file is not what I want...

Pollution Attacks There are many ways to pollute files: - Corrupting the majority content of the files - Corrupting the block downloading at 99%

100,000 polluted files Background NO Name Ava/Source 100 files foo A 1 Foo1 70/78 2 foo1 60/66 3 foo 11/40 4 Foo-4 2/3 5......

100 files 100,000 polluted files foo Background NO Name Ava/Source 1 Foo1 70/78 It is high possible for customers to 2 foo1 60/66 search bad files! 3 foo 11/40 A 4 Foo-4 2/3 5......

Background Why there are so many pollution attacks: - Publishers (e.g., music companies) want copyright - Employ many programmers to launch these attacks red P2P network (Gnutella, KaZaA, etc.) The majority of content is polluted: - The service quality of P2P content sharing became low - Users gave up P2P content sharing systemsp2p network (Chord, CAN, Pastry, etc.) - More (Hybrid P2P, BitTorrent, etc.)

Lecture Roadmap Background Reputation Systems Case Study: Credence

Reputation Systems

Reputation Systems What is a reputation system: - Rating users or objects based on historical activities - Like credit cards - Assumption: high reputation users publish good content red P2P network (Gnutella, KaZaA, etc.) Types of reputation systems: - Global reputation model, e.g., PageRank - Personalized reputation model, e.g., EigenTrust and Credence Global reputation model is mainly used in centralized systems Personalized reputation is mainly used in P2P systems

How a reputation system works? Messages Author (Score) Votes........................

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (0) 0..................

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (0) 0.................. Bob Eve Dave

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (0) Like: 3.................. Bob Eve Dave

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3.................. Vi=1+1+1=3 Bob Eve Dave

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3 Don t play with AlphaGo Alice (3) 0............

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3 Don t play with AlphaGo Alice (3) 0 I hate C++ Bob (1) 0......

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3 Don t play with AlphaGo Alice (3) 0 I hate C++ Bob (1) 0...... Alice Dave

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3 Don t play with AlphaGo Alice (3) 0 I hate C++ Bob (1) Dislike: 2...... Alice Dave

How a reputation system works? Messages Author (Score) Votes I like Yale Alice (3) Like: 3 Don t play with AlphaGo Alice (3) 0 I hate C++ Bob (-1) Dislike: 2...... Vi=1-1-1=-1 Alice Dave

Examples

Global Reputation System Every entity has only one reputation score red P2P network (Gnutella, KaZaA, etc.) PageRank is a global peer-based trust model - Who plays the peer role in this context? - What are the historical activities of peers in this context?

PageRank [WWW 98] A B C

PageRank [WWW 98] A The number of web pages N = 3 The damping parameter d = 0.7 PR(A)=(1-d)x(1/N)+dx(PR(C)/a) PR(B)=(1-d)x(1/N)+dx(PR(A)/b) PR(C)=(1-d)x(1/N)+dx(PR(B)/c) B C PR(A)=0.1+0.7xPR(C) PR(B)=0.1+0.7xPR(A) PR(C)=0.1+0.7xPR(B) By solving the linear equations: PR(A)=PR(B)=PR(C)=0.33

PageRank [WWW 98] B A C The number of web pages N = 3 The damping parameter d = 0.7 PR(A)=(1-d)x(1/N)+dx(PR(C)/a) PR(B)=(1-d)x(1/N)+dx(PR(A)/b) PR(C)=(1-d)x(1/N)+dx(PR(B)/c) # of outgoing links PR(A)=0.1+0.7xPR(C) PR(B)=0.1+0.7xPR(A) PR(C)=0.1+0.7xPR(B) By solving the linear equations: PR(A)=PR(B)=PR(C)=0.33

PageRank [WWW 98] A A B C B C What are PageRanks? What s the problem in this example?

Personalized Reputation Model We focus on personalized reputation model Used in P2P content sharing systems Three different types: - Peer-based reputation systems, e.g., EigenTrust [WWW 03] - Object-based reputation systems, e.g., Credence [NSDI 06] - Hybrid reputation systems, e.g., Scrubber [P2P 07] Request Decentralized file-sharing system Alice

EigenTrust [WWW 03] EigenTrsut is the first peer-based reputation system: - Similar to PageRank - Each peer is assigned a personalized reputation score - Assumption: Good peer does not publish polluted files Problems: - Relying on recommenders - Cannot offer fine-grained reputation to each object - Difficult to decide parameters in practice

Lecture Roadmap Background Reputation Systems Case Study: Credence

Credence [NSDI 06] Credence is the first object-based reputation system: - In Alice s view, each object is assigned a reputation score - Defending against malicious recommenders - Fine-grained reputation for each object

Credence [NSDI 06] Files Providers Voters F10 P2, P6 P4 (+1), P6 (-1) F22 P2, P6, P8 P2 (-1), P7 (-1) F4 P2, P4 P2(+1), P4(-1), P7(-1) Alice F6 P11, P13, P14 P11(+1)..................

Computing each file s reputation Files Providers Voters F10 P2, P6 P4 (+1), P6 (-1) F22 P2, P6, P8 P2 (-1), P7 (-1) F4 P2, P4 P2(+1), P4(-1), P7(-1) Alice F6 P11, P13, P14 P11(+1)..................

A file s reputation For Alice, the reputation score of each object is computed by weighted average of voters similarities. n Rep(F) = V i θ( Alice,Voter_i ) i=1 n θ( Alice,Voter_i ) i=1 [ -1, 1 ]

A file s reputation For Alice, the reputation score of each object is computed by weighted average of voters similarities. n The vote cast by voter i on F (+1 or -1) Rep(F) = V i θ( Alice,Voter_i ) i=1 n θ( Alice,Voter_i ) i=1 [ -1, 1 ]

How to compute similarity Sim = (p-ab) a(1-a)b(1-b) For the overlapping voting set (e.g., S) between Alice and C i : - a is # of positive votes cased by Alice on the files in S divided by # of all the votes casted by Alice on the files in S - b is # of positive votes casted by C i on the files in S divided by # of all the votes casted by C i on the files in S - p is # of positive votes casted by both Alice and C i on the files in S divided by # of all the votes agreed by both Alice and C i on the files in S

How to compute similarity Sim = (p-ab) a(1-a)b(1-b) Simplify it! Sim(A, B) = # of the same votes on S S where S is the set consisting of overlapping files voted by both A and B

Example B: +1 C: -1 D: +1 File1 A: +1 B: +1 C: -1 File2 A: -1 B: -1 File3 A: -1 C: +1 D: -1 File4 A C: +1 D: -1 File5 A: +1 D: -1 File6 C: +1 G: -1 File7

Practical Issues There are several practical issues in Credence: - Cold start - Lack of enough overlapping voting history

Solution: Flow-based Reputation I want to compute similarity with C, but I do not have direct similarity with C A C 0.8 0.9 B

Solution: Flow-based Reputation I want to compute similarity with C, but I do not have direct similarity with C 0.8 * 0.9 = 0.72 A C 0.8 0.9 B

Credence [NSDI 06] Files Providers Voters F10 P2, P6 P2, P4, P6 F22 P2, P6, P8 P2, P7 F4 P2, P4 P2, P4, P7 Alice F6 P11, P13, P14 P11..................

Credence [NSDI 06] Files Providers Voters F10=0.8 P2, P6 P2, P4, P6 F22 P2, P6, P8 P2, P7 F4 P2, P4 P2, P4, P7 Alice F6 P11, P13, P14 P11..................

Credence [NSDI 06] Files Providers Voters F10=0.8 P2, P6 P2, P4, P6 F22=0.5 P2, P6, P8 P2, P7 F4 P2, P4 P2, P4, P7 Alice F6 P11, P13, P14 P11..................

Credence [NSDI 06] Files Providers Voters F10=0.8 P2, P6 P2, P4, P6 F22=0.5 P2, P6, P8 P2, P7 F4=0.9 P2, P4 P2, P4, P7 Alice F6 P11, P13, P14 P11..................

Credence [NSDI 06] Files Providers Voters F10=0.8 P2, P6 P2, P4, P6 F22=0.5 P2, P6, P8 P2, P7 F4=0.9 P2, P4 P2, P4, P7 Alice F6=0.6 P11, P13, P14 P11..................

Credence [NSDI 06] Pick the highest reputation file Files Providers Voters F10=0.8 P2, P6 P2, P4, P6 F22=0.5 P2, P6, P8 P2, P7 F4=0.9 P2, P4 P2, P4, P7 Alice F6=0.6 P11, P13, P14 P11..................

Discussion: Does it work? Credence works under assumptions: - Malicious nodes publish many polluted files - Malicious users cast misleading votes - # of malicious users should not be higher than 1/2, say 20% - There is no large-scale sybil attack in the setting

Next Lecture In the lec-6, I will cover: - What is sybil attack - How to defend against sybil attacks - Case studies: SybilGuard and DSybil