Reputation Management Survey. - Vinay. Introduction. Electronic markets, Distributed peer-to-peer applications - other forms of online collaboration All based on mutual trust, which enables transacting peers to overcome the uncertainty and risk inherent in the environment.
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Where does this Trust Come From ????
But the credibility of this kind of Information must be critically
assessed as it is third party information.
- The Main goal of these systems is predicting a peer’s future actions,
given knowledge about its past behavior
- From Where do they get this Knowledge from ????
Here we are going to analyze the Maturity of current reputation systems . We are going to compare 11 reputation systems in terms of
A node which is gathering recommendation information selects its recommenders from a set of possible nodes or it can simply allow and expect any knowledgeable node provide.
The node which is gathering can be trustor ie a centralized entity collecting all recommendations.
The main criterion to consider for selection is the credibility of the recommender
3. Interpretation and reasoning
The acquired recommendations are combined with local experience ( trustor’s local experience ) and aggregated into suitable format.
Finally the system may analyze the overall credibility of the Information.
These Reputation systems represent a wide range of applications with
eBay Systems They are
This reputation system stores users ratings linked to their profiles and elated transactions but leaves credibility analysis to human users
This also leaves the credibility analysis to humans but apart from that it also performs a automated credibility analysis as well.
FuzztTrust and REGRET
Both designed for same purpose multi agent market place. But have different approaches.
FuzzyTrust Systems They are
In This reputation system, Local trust scores are generated and then added to global reputation values
Viewpoints are applied as needed to enter a local reputation view , based on social relations between peers.
For Cooperative applications , Trustors are given signed receipts of successful transactions ie cookies as sign of trust
MDNT Systems They are
In This reputation estimate involves predicting trustee’s behavior
based on experience from a specific time period
This uses probabilistic approach and considers the probability of recommenders to provide incorrect information
It considers transaction and community contexts when estimating reputation
Managing Trust Systems They are
This reputation system considers only negative experiences and allows recommenders to remain anonymous
This is file sharing system , It relies specifically on a global , shared view of reputation
The Reputation information is used to choose the most trustworthy partner. It bases reputation expression on probability distributions.
When Creating a Recommendation , Recommender analyzes the local experience and also takes into consider the negative and Positive experience
In Managing Trust , it considers only negative experience
And is the only system which allows sources of complaints anonymous. It also uses digital signature
NICE and Unitec
They use aggregated opinions but they do not specify the aggregation method and simply assume a policy is in place for determining it . This policy can be shared among all peers or locally defined.
An opinion is calculated as the weighted average of single experience ratings with more weight to given to newer experiences.
Travos and EigenTrust
They keep counters of positive and negative experiences and use them to provide aggregated opinion.
Travos simply presents both counters.
Eigen Trust calulates their difference and normalizes the value between the range [0,1]
In this system , opinion presented is calculated by CCCI metric
CCCI Stands for Correlation Commit Clear Influence
For Commit , we define 7 levels of criteria ie -1 to 5
None or ignore
Nothing is delivered and so on to Fully Delivered all the Commitment
For Clear Criteria , we again define 7 levels of criteria -1 to 5
None or ignore
Not clear, barely clear, ….. Very clear
None or ignore
Unimportant , Barely , Partially , largely , important, very imp.
Then the Correlation is caluculated
Correlation is calculated as
= Σ(Commit criterion c * Clear criterion c * Inf criterion c )
This gives us how much the Trusted agent has delivered his commitment.
The reputation System resides on a centralized server. It shows all ratings as part of user profiles and user can add their comment on it.
This is same as eBay but suggests to have time stamps to be included for the recommendation part as a part of the key for storing it. It also includes recommenders digital signature to guarantee its integrity.
Out of the 11 reputation systems Five of them uses recommendations from all peers for selection . They are
Two of them Limit the number of recommenders
which groups peers according to their social context such as their relationship with trustee and then chooses the most representative member of each group
Recommenders are chosen through global weighting based on their number of performed transactions and local trust score. The weight is then compared to a threshold based on peer’s role , A Superpeer with high threshold than regular peer for load balancing.
In this recommenders are selected based on their credibility. Which lowers the likelihood of new recommenders to get their voice
It may have bias in selection as it is left to the user to decide.
Reputation estimation is based only on existing recommendations but selection method for this s not defined.
The most common way is to leave it to the trustor for slection.
Here the trustee tries to locate chains of first hand recommendations from trustor to itself
Once the Reputation Information is collected , it must be aggregated into a suitable format then we examine how recommendations are aggregated and interpret them.
The Final Interpretation of the result is most commonly threshold based.
If the trustee’s reputation value is high enough , the trustor will decide to transact with it.
This Approach is taken by NICE, MDNT, Managing Trust and MLE
FuzzyTrust, REGRET and PeerTrust do not specify how the final measure is interpreted but both threshold and rank based approaches are possible.
Travos Explicitly uses rank based approach where a trustor orders a group of Potential partners based on their reputation for selection purpose.
Generally Rank Based selection is only usable when several potential partners can provide a similar service and therefore interchangeable.
eBay and Unitec do not perform interpretation at all as they present a report to a human user
Evaluating the result final measure is interpreted but both threshold and rank based approaches are possible.
Evaluation is done rarely . Ad not all the systems do evaluation
REGRET calculates a Reliability value for each type of reputation based on variety of factors such as available information, social relationships .
eBay and Unitec provide reports for which given metadata evaluation is possible
Conclusion final measure is interpreted but both threshold and rank based approaches are possible.
There are two points of focus for designing a reputation system to perform well in its field.
Social Requirements & Scalability
In this survey we have focused on analyzing the Social Requirements but there was not sufficient information available on Scalability of the analyzed systems.
Scalability requires addressing three load challenges. Load placed on trustor, high reputation nodes and network through recommendation transfers.
Allowing different levels of analysis based on situation is a solution for the above mentioned loads in Scalability
Thank You final measure is interpreted but both threshold and rank based approaches are possible.