SORT Self Organizing trust model for peer to peer system
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This document presents a trust model for peer-to-peer networks that aims to mitigate attacks from malicious peers. It defines two contexts to measure trustworthiness: service and recommendation. Peers calculate trust values for other peers based on past interactions and recommendations from acquaintances. The model includes metrics for service trust, reputation, and recommendation trust to enable peers to assess the trustworthiness of others in their network based on local information.
SORT Self Organizing trust model for peer to peer system
- 2. Anonymous nature of peer-to-peer (P2P) systems exposes them to malicious activity. Building trust relationships among peers can mitigate attacks of malicious peers. This project enable a peer to reason about trustworthiness of other peers based on their past interactions. Peers create their own trust network in their proximity by using local information available and do not try to learn global trust information.
- 3. • Two contexts of trust, service, and recommendation context, are defined to measure trustworthiness in providing services and giving recommendations. • Interactions and recommendations are evaluated based on importance, recentness, and peer satisfaction parameters.
- 4. Calculated trust information is not global and does not reflect opinions of all peers. Classifying peers as either trustworthy or untrustworthy is not sufficient in most cases. Metrics should have precision so peers can be ranked according to trustworthiness. Trust models on P2P systems have extra challenges comparing to e-commerce platforms. Malicious peers have more attack opportunities in P2P trust models due to lack of a central authority
- 5. Recommendation-based attacks were contained except when malicious peers are in large numbers, e.g., 50 percent of all peers. Experiments on SORT show that good peers can defend themselves against malicious peers metrics let a peer assess trustworthiness of other peers based on local information. Service and recommendation contexts enable better measurement of trustworthiness in providing services and giving recommendations.
- 7. Service Trust Metric Reputation Metric Recommendation Trust Metric Selecting Service Providers
- 8. When evaluating an acquaintance’s trustworthiness in the service context, a peer first calculates competence and integrity belief values using the information in its service history. Competence belief represents how well an acquaintance satisfied the needs of past interactions .Let friend request denote the competence belief of pi about pj in the service context. Average behavior in the past interactions is a measure of the competence belief. Consistency is as important as competence. Level of confidence in predictability of future interactions is called integrity belief. Let I bij denote the integrity belief of pi about pj in the service context. Deviation from average behavior (cbij) is a measure of the integrity belief.
- 9. The reputation metric measures a stranger’s trust worthiness based on recommendations. In the following two sections, we assume that pj is a stranger to pi and pk is an acquaintance of pi. If pi wants to calculate rij value, it starts a reputation query to collect recommendations from its acquaintances. Trustworthy acquaintances and requests their recommendations. Let max denote the maximum number of recommendations that can be collected in a reputation query and jSj denote the size of a set S. In the getting recommendation algorithm, pi sets a high threshold for recommendation trust values and requests recommendations from highly trusted acquaintances first. Then, it decreases the threshold and repeats the same operations.
- 10. Eg: Facebook. Assume that pi wants to get a particular service. pj is a stranger to pi and a probable service provider. To learn pj’s reputation, pi requests recommendations from its acquaintances. Assume that pk sends back a recommendation to pi. After collecting all recommendations, pi calculates rij. Then, pi evaluates pk’s recommendation, stores results in RH ik, and updates rtik. Assuming pj is trustworthy enough, pi gets the service from pj. Then, pi evaluates this interaction and stores the results in SH ij, and updates stij.
- 11. When pi searches for a particular service, it gets a list of service providers. Considering a facebook application, either post share the links to other peer. Connecting the all people with recommendation multiple peers, checking integrity is a problem since any file part downloaded from an uploader might be inauthentic. Service provider selection is done based on service trust metric, service history size, competence belief, and integrity belief values. When pi wants to download a file, it selects an uploader with the highest service trust value