Different methods and Conclusions

1
Different methods and Conclusions

• Liqin Zhang

2
Different methods

• Basic models
• Reputation models in peer-to-peer networks
• Reputation models in social networks

3
Rating systems

• Reputation is taken to be a function of the
  cumulative positive or negative rating for a
  seller or buyer
• Rating model
• Uniform context environment heard rating from
  one agent
• Multiple context environment from multiple
  agents
• Centrality-based rating based on in/out degree
  of a node
• Preference-based rating Consider the preferences
  of each member when selecting the reputable
  members
• Bayesian estimate rating to compute reputation
  with recommendation of different context

4
Basic models

• Computational model
• Based on how much deeds exchanged
• Collaborative model
• Based on recommendation from similar tasted people

5
Computational model2

• If Reputation increase, trust increase
• If trust increase, reciprocity increase
• If reciprocity increase, reputation increase

Reputation
Reciprocity mutual exchange of deeds
Net benefit
Reciprocity
Trust
6
A Collaborative reputation mechanism

• Collaborative filtering
• To detect patterns among opinions of different
  users
• Make recommendation based on rating of people
  with similar taste
• Fake rating
• 1. Rate more than once
• 2. Fake identity
• Solve rating from people with high reputation in
  network weighted more

7
Reputation model in peer-to-peer11

• P2P network
• peers cooperate to perform a critical function
  in a decentralized manner
• Peers are both consumers and providers of
  resources
• Peers can access each other directly
• Allow peers to represent and update their trust
  in other peers in open networks for sharing files

8
Models in peer-to-peer networks

• Based on recommendation from other peers
• Combine with Bayesian network
• Based on global trust value

9
Method 1 Reputation based on recommendation 11

10

• Recomendation from different kind of peers
• Different weight
• Update references weight
• Final reputation and trust is computed based on
  Bayesian network
• Solve reputation on different aspects of a peer

11
Method2 based on global trust value---Eigen
Trust Algorithm12

• Decreases the number of downloads of
  unauthenticated files in a peer-to-peer file
  sharing network by assigning a unique global
  trust value
• A distributed and secure method to compute global
  trust values based on power iteration
• Peers use these global trust values to choose the
  peers from whom they download and share files

12
Reputation Peer to Peer N/w

• Limited Reputation Sharing in P2P Systems14
• Techniques based on collecting reputation
  information which uses only limited or no
  information sharing between nodes.
• Effect of limited reputation information sharing
  in a peer-to-peer system.
• Efficiency
• Load distribution and balancing
• Message traffic

13
Reputation models in Social networks310

• Social network
• a representation of the relationships existing
  within a community
• Each node provide both services and referrals for
  services to each other

14
Importance of the nodes

• Proposal 1 all nodes are equal important
• Proposal 2 some nodes are important than others
• Referrals from A, B, C,D,E is more important than
  those nodes in only local network pivot
• You may trust the referral from a friend of you
  than strangers
• You may also need consider the your preference
  regarding to referral

15
Models in social network

• Reputation extracting model
• Ranking the reputation for each node in network
  based on their location
• Social ReGreT model
• Based on information collected from three
  dimension

16
Reputation models in Social networks

• Extracting Reputation in Multi agent systems8
• Feedback after interaction between agents
• Also consider the position of an agent in social
  network
• Node ranking creating a ranking of reputation
  ratings of community members
• Based on the in-degree and out-degree of a node
  (like Pagerank)

17
Reputation models in Social Networks

• Social ReGreT5
• Analysis social relation
• To identify valuable features in e-commerce
• Aimed to solve the problem of referrers false,
  biased or incomplete information
• Based on three dimensions of reputation
• If use only interaction inf. --- individual
  dimension(single)
• If also use inf. from others --- social dimension
  (multiple)
• Three dimension
• Witness reputation from pivot agents
• Neighborhood reputation
• System reputation default reputation value based
  on the role played by the target agent

18
Conclusions

• Reputation is very important in electronic
  communities
• Reputation can have different notation such as
  general estimate a person, perception that an
  agent has of anothers intentions and norms
• Reputation systems can be grouped according to
  the nature of information they give about the
  object of interest and how the rating is
  generated, 4 reputation systems are discussed

19
Conclusions

• Reputation can be classified to individual and
  group reputation, individual reputation can be
  further classified
• The challenge for reputation includes less
  feedback, negative feedback, un-honesty feedback
  (change name), context and location awareness
• An agent can be honesty, malicious, evil, selfish
• Discussed 7 metrics with benchmarks

20
Conclusions Comparison methods

• Basic models
• Computation model
• based on how much deeds exchanged
• Can be used in P2P and Social network
• Doesnt consider references/recommendation,
  weight of deeds
• Collaborative model
• Based on the recommendation from similar tasted
  people
• Recommendation is weighted based on referrers
  reputation avoid fake recommendation
• Doesnt consider the location of referrer

21
Conclusions Comparison methods

• In P2P network,
• Bayesian network model
• Based on information collected from friends
• Peers share recommendations
• It allows to develop different trust regarding to
  different aspects of the peers capability
• Overall trust need combine all aspect
• Doesnt consider location

22
Conclusions Comparison methods

• In social network
• Can consider the position of an agent, Pivot
  agents are more important than other agents
• NodeRanking
• Ranking the reputation in social network based on
  position
• Used to find the pivot
• Social ReGreT model
• Consider three dimension
• Witness pivot node
• Neighborhood recommendation
• System value

23
Conclusions

• The reputation computation need consider
  recommendation of friends, the position of the
  referrer, weight for referrer
• friends may refer to its neighborhood, or the
  group of people who has the similar taste, or
  people you trust
• Weight for referrer can avoid fake recommendation
• No models consider all of the factors

24
References

• 1. Computational Models of Trust and
  Reputation Agents, Evolutionary Games, and
  Social Networks, www.cdm.csail.mit.edu/ftp/lmui/
  computational20models20of20trust20and20reputa
  tion.pdf
• 2. A computation model of Trust and
  Reputation, http//csdl2.computer.org/comp/proceed
  ings/hicss/2002/1435/07/14350188.pdf
• 3. Trust and Reputation Management in a
  Small-World Network, ICMAS Proceedings of the
  Fourth International Conference on MultiAgent
  Systems (ICMAS-2000), 2000
• 4. How Social Structure Improves Distributed
  Reputation Systems, http//www.ipd.uka.de/nimis/p
  ublications/ap2pc04.pdf
• 5. Social ReGreT, a reputation model based on
  social relations , ACM SIGecom Exchanges Volume 3
  ,  Issue 1   Winter, 2002,Pages 44 56
• 6. Detecting deception in reputation
  management, Proceedings of the second
  international joint conference on Autonomous
  agents and multiagent systems , 2003

25
References

• 7. Finding others online reputation systems
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• 8. J. Pujol and R. Sanguesa and J. Delgado,
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26
References

• 12 S. D. Kamvar, M. T. Schlosser, and H.
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• 15 Lik Mui, Computational Models of Trust and
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27
References

• 17 G.L. Rein, Reputation Information Systems A
  Reference Model, Proceedings of the 38th Hawaii
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