Trust and Reputation System by 0lM564c

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									UCDavis, ecs251
  Fall 2007




           Trust and Reputation System

                              S. Felix Wu
                     University of California, Davis

                          wu@cs.ucdavis.edu
                     http://www.cs.ucdavis.edu/~wu/



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UCDavis, ecs251
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                           Computational Trust



                                    Trust Attribute




                     representing a trust relationship between two directly
                     communicating entities



        11/29/2007                  Trust and Reputation System               2
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                          Computational Trust


         • Trust Values
                  – I “trust” him “50/50”.
                  – I trust him “0.715”


         • Partial Ordering Relationship
                  – “I trust Alice more (than Bob)”
                  – “I trust Alice more than the set threshold of
                    my spam mail filter”



        11/29/2007               Trust and Reputation System        3
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                              Trust Ordering


         • Trust Ordering
                  – I trust you, otherwise, I don’t.


         • Information-based Ordering
                  – I trust you, I don’t, or I don’t know based on
                    the information I have currently.
                  – Dynamics and Uncertainty




        11/29/2007                Trust and Reputation System        4
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                           Policy & Delegation


         • Policy:
                  – If X trusts Y by Z, then A will trust B by C.
                  – E.g.
                     • If Bank American will lend you $1M, then Washington
                       Mutual will lend you $2M.




        11/29/2007                 Trust and Reputation System           5
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                           Policy & Delegation


         • Policy:
                  – If X trusts Y by Z, then A will trust B by C.
                  – E.g.
                     • If Bank American will lend you $1M, then Washington
                       Mutual will lend you $2M.
                  – Trust means “Action and Risk”
                  – Computational Trust needs to quantify the
                    actions and their associated risks.
                  – It might be “Mutual Recursive” though…


        11/29/2007                 Trust and Reputation System           6
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                          Computational Trust


         • Direct DSL Link
                  – Observing our direct neighbor’s behavior


         • Indirect Sources in Social Network
                  – Trust delegation
                  – About a peer, may or may not be your direct
                    neighbor




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                                 Trust in P2P

         • The Service Provider provides a
           management system for trust and
           reputation
                  –   Google’s “PageRank”
                  –   Antivirus system
                  –   eBay’s seller reputation system
                  –   PKI
         • P2P -- everything hopefully to be P2P
                  – Decentralized model for trust


        11/29/2007                 Trust and Reputation System   8
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                     Cheating & Incentives


         • Selfish users in Gnutella and Bittorrent
         • eBay flaw seller ranking
         • Google page rank

         • Selfishness or Reputation boost




        11/29/2007         Trust and Reputation System   9
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                     P2P Trust Model


         • Less vulnerable?
         • Harder to implement? In a decentralized
           setting?




        11/29/2007      Trust and Reputation System   10
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                                     Problem


         • Problem:
                  – Reduce inauthentic files
                    distributed by malicious
                    peers on a P2P network.


         • Motivation:
                                   “Major record labels have
                                   launched an aggressive new
                                   guerrilla assault on the
                                   underground music networks,
                                   flooding online swapping
                                   services with bogus copies of
                                   popular songs.”
                                              -Silicon Valley Weekly
        11/29/2007                  Trust and Reputation System        11
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                               Problem

         • Goal: To identify sources
           of inauthentic files and
           bias peers against                                0.9
           downloading from them.

         • Method: Give each peer a
           trust value based on its
           previous behavior.



                                                                 0.1



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                       Some approaches


         •    Past History
         •    Friends of Friends
         •    EigenTrust
         •    PeerTrust
         •    TrustDavis




        11/29/2007         Trust and Reputation System   13
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                               Terminology


         • Local trust value: cij.                                                 Peer 3
              The opinion that peer i has of
              peer j, based on past                                        t3=.5
              experience.                                       Peer 1
         • Global trust value: ti.
              The trust that the entire                                     C12=0.3     C23=0.7
                                                       t1=.3
              system places in peer i.
                                                                         C21=0.6

                                                                           t2=.2
                                                        C14=0.01

                                                                                    Peer 2
                                                   t4=0

        11/29/2007                Trust and Reputation System                               14
                                                                Peer 4
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                        Local Trust Values


         • Each time peer i downloads an                              Cij=
           authentic file from peer j, cij
           increases.
         • Each time peer i downloads an
           inauthentic file from peer j,
           cij decreases.
                                                             Peer i          Peer j




        11/29/2007             Trust and Reputation System                     15
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                  Normalizing Local Trust Values


         • All cij non-negative
         • ci1 + ci2 + . . . + cin = 1


                    Peer 1
                                                         Peer 1


                             C12=0.9




                                                                     Peer 2   Peer 4
              C14=0.1

                                       Peer 2




        11/29/2007Peer 4               Trust and Reputation System                     16
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                                  Local Trust Vector



       • Local trust vector ci:                                0              0 
                                                                               
                                                                            
            contains all local trust values
            cij that peer i has of other
            peers j.                                                           0 .9 
                                                               0 
                                                                   Peer 2
                                                                                0 
                                                                               
                                                                                 0 .1 
                         Peer 1

                                                                             
                                  C12=0.9
                                                                 Peer 4

                     C14=0.1

                                        Peer 2
                                                                                  c1
                        Peer 4                                     Peer 1

        11/29/2007                           Trust and Reputation System               17
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                            Past history
                                                                0? 
                                                                
         • Each peer biases its choice                          0? 
           of downloads using its own
           opinion vector ci.                                   0? 
         • If it has had good past
           experience with peer j, it
                                                                
           will be more likely to                               
           download from that peer.
         • Problem: Each peer has
                                                                0? 
                                                                Peer 4



           limited past experience.
           Knows few other peers.
                                                      Peer 1    
                                                                 
                                                                
                                                                Peer 6



                                                                 ?
                                                                 0 
                                                                0? 
        11/29/2007            Trust and Reputation System          18
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                     Friends of Friends
                                                          0       0 
                                                                   
         • Ask for the opinions of                        0         
           the people who you                             0       0 
                                                                      Peer 2


           trust.                                                  
                                                                  0 
                                                          0 
                                                           Peer 4
                                                                    0 
                                                 Peer 1
                                                                   
                                                                0 
                                                           
                                                           Peer 6
                                                                     
                                                          0       0 
                                                          0        
        11/29/2007         Trust and Reputation System             
                                                                     19 8
                                                                      Peer
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                     Friends of Friends
                                                            0        0 
                                                                      
         • Weight their opinions                            0        0 
           by your trust in them.
                                                            0        0 
                                                                      
                                                                     0 
                                                            0 
                                                             Peer 4
                                                                         
                                                   Peer 1
                                                                      
                                                                   0 
                                                                            Peer 2




                                                             
                                                              Peer 4    
                                                            0        0 
                                                            0         
        11/29/2007         Trust and Reputation System
                                                                      
                                                                       20
                                                                            Peer 8
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                                       The Math

                           c 'ik   cij  c jk                      What they think of peer k.
                                          j

                                                       And weight each friend’s
                                                      opinion by how much you
                     Ask your friends j
                                                              trust him.

                          .1                                               .1
                                                                           .5
                                   
                          .3
                          .2                  0 .2 0 .3 0 .5 .1 0 0 0       0
                          .3                                                0
                          .1                                                0
                          .1                                               .2



                         c     '
                               i
                                                             C ci   T

        11/29/2007                     Trust and Reputation System                                21
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                     Problem with Friends                                  0 
                                                                           
                                                                           0 
                                                                                
                                                                           0 
                                                                               
                                                                               
                                                                           0 
                                                                               
                                                                             
                                                                               
         • Either you know a lot                         
                                                         
                                                              
                                                              
                                                                  0 
                                                                      
                                                                           0 
                                                                           0 
                                                                               
           of friends, in which                          0 
                                                         0 
                                                                    
                                                                  0 
                                                                           0 
                                                                      
           case, you have to
                                                                             
                                                                0        
                                                         0      0      0 
           compute and store
                                                                           
                                                              0      0 
                                                                       0 
           many values.                                  0 
                                                         
                                                         
                                                              
                                                              
                                                                  0 
                                                                  
                                                                  
                                                                       
                                                                       
                                                                           
                                                                           0 
                                                                                
                                                                               
         • Or, you have few                                                0 
                                                                           
                                                                           
                                                                                
                                                                                

           friends, in which case
           you won’t know many                           0 
                                                             
                                                                  0 
                                                                      
                                                                    
           peers, even after
                                                         0 
                                                         0      0 
                                                                    
           asking your friends.                          
                                                         0 
                                                                 0 
                                                                  0 
                                                                    
                                                              0 
                                                                    
                                                         0      0 
                                                         0          
                                                                    
        11/29/2007         Trust and Reputation System                     22
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                                 Dual Goal


         • We want each peer to:
                  – Know all peers.
                  – Perform minimal computation (and storage).




        11/29/2007               Trust and Reputation System     23
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                      Knowing All Peers


         • Ask your friends:                                               0 
           t=CTci.                                                         
                                                                           0 
                                                                           0 
                                                                                



         • Ask their friends:
                                                                               
                                                                               
                                                                           0 

           t=(CT)2ci.                                                          
                                                                             
                                                                               

         • Keep asking until the
                                                                           0 
                                                         0      0      0 
                                                                           
                                                         0        
           cows come home:                               0 
                                                             
                                                                  0 
                                                                         0 
                                                                               
           t=(CT)nci.
                                                                0        
                                                         0      0      0 
                                                                           
                                                              0      0 
                                                                       0 
                                                         0      0          
                                                         0          
                                                                       0 
                                                                               
                                                                           0 
                                                                               
                                                                               




        11/29/2007         Trust and Reputation System                              24
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                     Minimal Computation


         • Luckily, the trust vector t, if computed in
           this manner, converges to the same thing
           for every peer!
         • Therefore, each peer doesn’t have to store
           and compute its own trust vector. The
           whole network can cooperate to store and
           compute t.



        11/29/2007        Trust and Reputation System   25
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                     Non-distributed Algorithm


         • Initialize:                                                 T
                                      1                          1
                          t   (0)
                                                      ...
                                      n                          n
                                                                   
         • Repeat until convergence:



                                t (k1)  CTt (k)

        11/29/2007                  Trust and Reputation System            26
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                     Distributed Algorithm


         • No central authority to                                                              .1
                                             .1
           store and compute t.                                                                 .5
                                                        
                                             .3
         • Each peer i holds its own         .2                  0 .2 0 .3 0 .5 .1 0 0 0         0
           opinions ci.                      .3                                                  0
                                             .1                                                  0
         • For now, let’s ignore
                                             .1                                                 .2
           questions of lying, and let
           each peer store and
           compute its own trust                      ( k 1)
           value.                                 t   i         c t  (k )
                                                                   1i 1       ...  c t(k )
                                                                                     ni n




        11/29/2007              Trust and Reputation System                                27
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                     Distributed Algorithm

    For each peer i {
      -First, ask peers who know you for their opinions of you.
      -Repeat until convergence {
             -Compute current trust value: ti(k+1) = c1j t1(k) +…+ cnj tn(k)
             -Send your opinion cij and trust value ti(k) to your
              acquaintances.
             -Wait for the peers who know you to send you their trust
              values and opinions.
      }
    }




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                     Probabilistic Interpretation




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                     Malicious Collectives




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                      Pre-trusted Peers


         • Battling Malicious
           Collectives
         • Inactive Peers
         • Incorporating
           heuristic notions of
           trust
         • Convergence Rate




        11/29/2007         Trust and Reputation System   31
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                      Pre-trusted Peers


         • Battling Malicious
           Collectives
         • Inactive Peers
         • Incorporating
           heuristic notions of
           trust
         • Convergence Rate




        11/29/2007         Trust and Reputation System   32
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                      Secure Score Management


         • Two basic ideas:
                  – Instead of having a
                    peer compute and store                           ?       M
                    its own score, have
                                                                      Score Manager
                    another peer compute
                    and store its score.
                  – Have multiple score                    Distributed Hash Table
                    managers who vote on a
                    peer’s score.
                                                                             M
                                                                      ?
                                                                      ?      M
                                                                      ?      M
        11/29/2007                 Trust and Reputation System            Score Managers
                                                                                  33
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                  PeerTrust System Architecture



                                                              Trust Manager

                                                          Feedback        Trust
                              P1                         Submission     Evaluation


                     P6                     P2
                                                                                     Trust
                                                               Data Locator
                                                                                     Data
                          P2P Network
                           P2P Network


                     P5                     P3


                              P4


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                     How to use the trust values ti


         • When you get responses from multiple
           peers:
                  – Deterministic: Choose the one with highest
                    trust value.
                  – Probabilistic: Choose a peer with probability
                    proportional to its trust value.




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                           Load Distribution



           Deterministic Download Choice             Probabilistic Download Choice




        11/29/2007                Trust and Reputation System                        36
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                              Threat Scenarios

         • Malicious Individuals
                  – Always provide inauthentic
                    files.



         • Malicious Collective
                  – Always provide inauthentic
                    files.
                  – Know each other. Give
                    each other good opinions,
                    and give other peers bad
                    opinions.




        11/29/2007                   Trust and Reputation System   37
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                          More Threat Scenarios

         • Camouflaged Collective
                  – Provide authentic files some
                    of the time to trick good
                    peers into giving them good
                    opinions.



         • Malicious Spies
                  – Some members of the
                    collective give good files all
                    the time, but give good
                    opinions to malicious peers.




        11/29/2007                      Trust and Reputation System   38
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                     Malicious Individuals




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                     Malicious Collective




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                     Camouflaged Collective




        11/29/2007         Trust and Reputation System   41
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                     P2P Electronic Communities




        11/29/2007           Trust and Reputation System   42
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                     Motivation




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                      Motivation


         • Should we buy?
         • How do we decide?




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                     Motivation




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                                    Motivation


         • Should we buy?
         • How do we decide?

         • What we want:
                  –   accurately estimate risk of default
                  –   minimize the risk of default
                  –   minimize losses due to pseudonym change
                  –   avoid trusting a centralized authority
         • How do we achieve these goals?




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                             Motivation




         •        TrustDavis is a reputation system that
                  realizes these goals.

         •        It recasts these goals as the following
                  properties:



        11/29/2007            Trust and Reputation System   47
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                                       Motivation

         1.           Agents can accurately estimate risk
                  –      Third parties provide accurate ratings

         2.           Honest buyer/seller avoids risk (if possible)
                  –      Insure transactions

         3.           No advantage in obtaining multiple identities
                  –      Agents can cope with pseudonym change

         4.           No need to trust a centralized authority
                  –      No centralized services needed




        11/29/2007                      Trust and Reputation System   48
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                             Motivation



         Incentive Compatibility:

                  Each player should have incentives to
                  perform the actions that enable the
                  system to achieve a desired global
                  outcome.



        11/29/2007            Trust and Reputation System   49
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                                       Motivation

         1.           Agents can accurately estimate risk
                  –      Third parties provide accurate ratings

         2.           Honest buyer/seller avoids risk (if possible)
                  –      Insure transactions

         3.           No advantage in obtaining multiple identities
                  –      Agents can cope with pseudonym change

         4.           No need to trust a centralized authority
                  –      No centralized services needed

                                  Incentive Compatibility!


        11/29/2007                      Trust and Reputation System   50
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                         Motivation



         A Reference is:
               Acceptance of Limited Liability.



                                         $100
                     C      A                          B



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                                       Motivation

         1.           Agents can accurately estimate risk
                  –      Third parties provide accurate ratings
                  –      Parties are liable for the references they provide
         2.           Honest buyer/seller avoids risk (if possible)
                  –      Insure transactions
                  –      Buyers/sellers pay for references to insure their
                         transactions
         3.           No advantage in obtaining multiple identities
                  –      Agents can cope with pseudonym change
                  –      References are issued only to trusted identities
         4.           No need to trust a centralized authority
                  –      No centralized services needed
                  –      Anyone can issue a reference
                                        Use References!

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                             Outline



         • TrustDavis leverages social networks

         • For now, examples assume No False Claims (NFC)

         • The use of TrustDavis does NOT preclude trade
           outside the system.




        11/29/2007         Trust and Reputation System      53
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                     Paying for References



                                                50
                      150
                                                          100
                                                                50


                                       150




        11/29/2007          Trust and Reputation System              54
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                     Paying for References


    How much is vb willing to pay to insure the transaction?
       (No riskless profitable arbitrage criterion)
    Example:           • v wants to buy three shirts.
                            b
                      •    Shirts cost $100 each from a
                           trustworthy seller
                      •    Unknown seller offers shirts for
                           $50 each (but maybe they are
                           only worth $25).
                      •    vb would risk 3 x $50 = $150 in      $100 each
                           the transaction
                      •    vb can borrow and lend money at     Trust-me.com
                           rate r=1.25 through the period
                           of the transaction                  Blowout SALE!
                          For $30, vb can insure herself!

                                $150!                            $50 each!
        11/29/2007               Trust and Reputation System                55
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                           Paying for References



         To insure herself vb buys the shirts and a hedging portfolio as follows:

                  1.   Instead of buying 3 shirts for $50 each
                       she buys only 2, saving $50.

                  2.   The buyer, vb , adds $30 of her own money and lends the
                       resulting $80 at rate r = 1.25.




        11/29/2007                    Trust and Reputation System                56
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                     Paying for References


         On Success:
            –   vb obtains $100 from the loan and buys
                the 3rd shirt

         On failure:
            –     vb sells the two shirts for $25 each
            –    gets $100 from the loan.
            –    She obtains a total of $150

                         Thus, vb can insure herself for $30.




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                     Selling References




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                       Selling References


    Seen as an investment…

                                    K C      C
    On Success the ROI is:                1
                                     K        K

                                C
    On failure the ROI is:
                                K

         If repeated many times the insurer may go bankrupt. Assume the
         insurer has W dollars available to insure this transaction.




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                          Selling References


    Insurer maximizes the expected value of the growth rate of capital
       (Kelly Criterion).
                                              1
                                                 
                                        Wn  
                                               n
                                 R  E log   
                                        W0  
    For given:
                                                
          – probability of failure p,
          – a desired growth rate of capital R; and,
          – fraction of the total funds W being risked in a transaction.
              The insurer can obtain a lower bound on the premium C.




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                                                     Selling References


                                           Minimum Return/Risk Ration for Different Failure Probabilities
   Cost/Insured Value – C/K




                              11/29/2007           Insured Value as a fraction of total funds – f
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                     A Non-Exploitable Strategy

         Two Scenarios:
         • No False Claims - NFC
         • With False Claims - FC

                       False claims only change the probability p.
                      We can incorporate the cost of verification.

         Key Idea:

           Save part of the money obtained in successful transactions in
                           excess of the opportunity cost.




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                     A Non-Exploitable Strategy


         Example.
         The buyer, vb, has $190 to spend on 1 of 3
              options:
         1.   Buying 3 shirts from an unknown seller
              for $50 each and insuring the
              transaction for $40. She values each
              shirt at $100.
         2.   Buying 2 pairs of shoes from a reliable
              retailer for $70 each. She thinks
              each pair is worth $90.
         3.   Buying 1 game console for $150, from
              a reliable online shop. She values the
              console at $240.


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                     A Non-Exploitable Strategy

         vb’s valuation for each of the 3 options is:

         1.       Shirts: 100 x 3 + 0 (no cash leftover) = $300

         2.       Pairs of Shoes: 90 x 2 + 50 (cash) = $230

         3.       Console: 240 x 1 + 40 (cash) = $280

                     Gains in excess of the opportunity cost are:
                         300-280=$20.
                     Part of these $20 should be saved to insure
                         future transactions.




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                     A Non-Exploitable Strategy


         The Strategy:

         1.       Initially only provide references to known agents
                  or those that leave a security deposit.
         2.       Insure all trade through references provided by
                  trusted agents.
         3.       Do not provide more insurance than you can
                  recover. Charge at least the lower bound for
                  providing a reference.
         4.       Save part of the money received “in excess of
                  the opportunity cost”.



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                     A Non-Exploitable Strategy


                                                               50

                                                   50
                        150
                                                             100
                                                                    50

      OK!
      Failed!
     $10 saved to
                                          150
    provide future
     Payment made
      insurance               10
    automatically by
           v1




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                                   Outline


         • Motivation
         • The Model
                  – Buying references
                  – Selling references
         • A Non-Exploitable Strategy
         • Future Work
         • Conclusion
                  – Key ideas


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                                  Future Work


         • Simulation
                  – sensitivity to estimates of p
                  – growth rate of capital
                  – dynamic behavior


         • Price Negotiation
                  – should avoid “double spending” problem
                  – fair distribution among insurers of the premium paid




        11/29/2007                   Trust and Reputation System           68
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                                   Outline


         • Motivation
         • The Model
                  – Buying references
                  – Selling references
         • A Non-Exploitable Strategy
         • Future Work
         • Conclusion
                  – Key ideas


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                             Conclusion


         TrustDavis provides:
         • Accurate Ratings
         • Non-exploitable strategy for honest agents
         • Pseudonym change tolerance
         • Decentralized infrastructure

                     Through the use of References.


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                                Conclusion


         Key Ideas:

         • Incentive Compatibility
                  – Incentive to accurately rate
                  – Incentive to insure
                  – No incentive to change pseudonym

         • Saving gains in excess of the opportunity
           cost to insure future transactions.


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                      The End




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