A trust aggregation portal


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A trust aggregation portal

  1. 1. A Trust Aggregation PortalSupervisor:Prof. Gihan Dias 128202E – A.G.E.A. Abeynayake
  2. 2. Social Identity & Trust• What is Social Identity? – Layman View – Cyberspace• Where does Trust fits in? Image Courtesy of: Social Identity Report at http://www.veechi.com/sir.html
  3. 3. Problem Definition• Numerous interconnections between individuals andOrganizations – Multiple Identities• Enormous social networking sites – Overlapping Content – Non-overlapping Content• Isolated nature of individual systems – Has there own Recommendation system – Personal Profile information – Not Disclosed to other systems
  4. 4. A Step Ahead...• A “Trust mark” for Individuals  Feedback by Friends  Day-to-Day Activities  Context Information Image Courtesy of: Social Identity Report at http://www.veechi.com/sir.html
  5. 5. Literature Review – Area of Concern• Study about NSTIC – National Strategy for Trusted Identities in Cyberspace• Collecting Aggregating User Data – Extraction Methods – Current Systems• Trust and Reputation Models – Existing models for Trust Scoring
  6. 6. NSTIC Study• NSTIC Addresses two central problems [1] – Passwords are inconvenient and insecure – Individuals are unable to prove their true identity online for significant transactions• Key Operational Roles – subjects – relying parties – identity providers – attribute providers – accreditation authorities
  7. 7. Identity Ecosystem [2]• NSTIC provides a framework for individuals and organizations to utilize secure, efficient, easy-to-use• and interoperable identity solutions to access online services in a manner that promotes• confidence, privacy, choice and innovation.
  8. 8. Image courtesy: www.ftc.gov/bcp/workshops/.../personalDataEcosystem.pdf
  9. 9. Collection and Aggregation of data• Prying Data out of a Social Network [3] presents Methods Of Extracting Data – Public Listings – Facebook and LinkedIn APIs – Facebook Graph API – Scraping Facebook UI
  10. 10. Collection and Aggregation of data• Crawling Social Networks [4] – crawled these user data in a breadth-first fashion – used a queue data structure – Stored in MySQL database using a simple schema
  11. 11. Trust and Reputation• Trust Classes [5] - According to Grandison & Slomans classication• Classification of trust and reputation measures Specific General Subjective Survey eBay, voting questionnaires Objective Product tests Synthesised general score from product tests, D&B rating
  12. 12. Trust and Reputation• Reputation Network Architectures [5] – Centralised Reputation Systems – Distributed Reputation Systems• Classification of Reputation Systems [6] – Flat reputation systems – Recursively weighting reputation system – Personalized reputation system with trust anchor
  13. 13. Trust and Reputation• Algorithms and Methods for Measurement [7] [8] – Collaborative filtering and Collaborative Sanctioning – Bayesian Networks – Fuzzy Decision Trees – Boosting algorithms
  14. 14. References[1] National Institute Of Standards And Technology. “Recommendations For Establishing An Identity Ecosystem Governance Structure”. White House.Department of Commerce. Available at: http://www.nist.gov/nstic/2012-nstic-governance-recs.pdf[2] NSTIC. “Enhancing Online Choice, Efficiency, Security, and Privacy”. Available at: http://www.whitehouse.gov/sites/default/files/rss_viewer/NSTICstrategy_ 041511.pdf[3] Joseph Bonneau, Jonathan Anderson, George Danezis. "Prying Data out of a Social Network", ASONAM 09 Proceedings of the International Conference on Advances in Social Network Analysis and Mining, 2009, pp. 249-254[4] D. H. Chau, S. Pandit, S. Wang, and C. Faloutsos, “Parallel Crawling for Online Social Networks,” in WWW ’07: Proceedings of the 16th international conference on World Wide Web, 2007, pp. 1283–1284.
  15. 15. References[5]B. Krishnamurthy and C. E. Wills, “Characterizing Privacy in Online Social Networks,” in WOSN: Workshop on Online Social Networks, 2008, pp. 37 – 42.[6] W. Xu, X. Zhou, and L. Li, “Inferring Privacy Information via Social Relations,” International Conference on Data Engineering, 2008.[7] A. Gutscher, J. Heesen and O. Siemoneit, “Possibilities and Limitations of Modeling Trust and Reputation,” Proc. CEUR Workshop, 2008.[8]Audun Jøsang, Roslan Ismail, and Colin Boyd. 2007. A survey of trust and reputation systems for online service provision. Decis. Support Syst. 43, 2 (March 2007), 618-644.