Declustering the iTrust Search and Retrieval Network to Increase Trustworthiness

1. Declustering the iTrust Search and Retrieval Network to Increase Trustworthiness Presentation by Christopher Badger Research conducted in collaboration with Yung-Ting Chuang, Isai Michel Lombera, Louise E. Moser and P. M. Melliar-Smith University of California, Santa Barbara Supported in part by NSF Grant CNS 10-16103

2. Overview • Introduction • Design and Implementation of iTrust • Peer Neighborhoods • Declustering Algorithm • Clustering Coefficients • Results and Analysis • Expectation of Cooperation • Conclusions and Future Work

3. Introduction • Search engines such as Google and Yahoo! have played an increasingly important role in today's world • They offer fast and accurate search results … ideally • They are centralized, and therefore vulnerable to: • Attack • Censorship • iTrust is our solution to this problem • iTrust is a P2P network that functions by distributing metadata about documents and search requests to random nodes in the iTrust membership • Designed to be resistant to censorship and attack

4. Source of Information Design of iTrust

5. Source of Information Request Encounters Metadata Requester of Information Design of iTrust

6. Source of Information Requester of Information Design of iTrust Request Matched

7. HTTP Implementation of iTrust

8. We define the neighborhood of a node as: all of the other nodes to which the node is directly connected Importance of a node's neighborhood The flow of information Neighborhoods cannot be unlimited in size Too expensive to track the entire network Neighborhoods

9. Neighborhoods A Green nodes comprise peer A's neighborhood

10. Neighborhoods • Beneficial to have only trustworthy nodes in one's neighborhood • How to determine which nodes are trustworthy? • How to define trustworthiness? • Randomness • Why is a random neighborhood useful? • How to achieve neighborhood randomness? • Declustering Algorithm

11. Declustering Algorithm • The process • Ask all current neighbors for a list of their neighbors • Create a master list containing all of these gathered lists • Ensure the list contains only unique peers • Drop all existing connections, effectively clearing the neighborhood • Select new neighbors randomly from the obtained list • Can be done in a manner similar to the binomial distribution, where each node has an equal chance to become a neighbor

12. Declustering Algorithm

13. What Declustering Does • Randomizes each node's neighborhood • Reduces the clustering coefficient of the node performing declustering • The clustering coefficient is a measure of how cliquish the network is • Is performed locally by each node • Does not require a global context • Lowers the expectation of cooperation

14. Metrics • Local clustering coefficient is defined as: • To calculate the local clustering coefficient of node X, put all of X's neighbors into a set S • Find E, the number of possible edges between all nodes in S • For an undirected graph, this number is: • Find e, the number of edges that exist between nodes in S • The local clustering coefficient for X is given as: • Global clustering coefficient is defined as: • The average of all of the local clustering coefficients │S│x(│S│- 1)

15. Metrics • Maximum degree of any node in the network • The number of connections of the most connected node in the network • Used as a reference for the prevalence of hubs in the network • Match probability • The probability that an iTrust search in the particular graph results in a hit • Network view • The cardinality of the set containing all of a node's neighbors and the neighbors of those neighbors • Average network view • The average of all nodes' network views

16. Results Watts-Strogatz Graph Barabási–Albert Graph Erdős–Rényi Graph

17. Results

18. Results

19. Results and Analysis • Declustering reduced the clustering coefficient in both the Watts-Strogatz and Barabási–Albert graphs • Declustering evened out the degree distribution in the network, acting to eliminate any hubs • For the Watts-Strogatz graph, the iTrust match probability greatly increased • Overall, declustering was able to effectively turn the Watts-Strogatz and Barabási–Albert graphs into random graphs similar to the Erdős–Rényi graph • By promoting network randomization, the minimum expectation of cooperation was decreased, thereby increasing robustness

20. Expectation of Cooperation • Definition: • Subjectively, the degree to which nodes act or rely on information provided by other nodes • Minimum Expectation of Cooperation • The minimum degree of cooperation expected from all nodes in order for the network to function well • Importance • A lower minimum expectation of cooperation allows nodes in the network to continue functioning well, despite increased resistance or attack by others

21. Expectation Of Cooperation

22. Conclusions • The declustering strategy increases iTrust's trustworthiness by randomizing peer neighborhoods • Declustering also decreases the global clustering coefficient of the network, which helps improve message forwarding performance • iTrust can be valuable for people who seek information on the Internet and are wary of potential censorship

23. Future Work • We are looking into combining declustering and different message relaying strategies to increase network robustness • In addition to the HTTP implementation, we are also developing an SMS implementation for iTrust • We intend to release all iTrust source code and related documentation to the general public

24. Questions? • Our iTrust Web Site • http://itrust.ece.ucsb.edu • Contact Information • Christopher Badger: badger.christopher.m@gmail.com • Yung-Ting Chuang: ytchuang@ece.ucsb.edu • Isai Michel Lombera: imichel@ece.ucsb.edu • Our project is supported by NSF CNS 10-16193