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A Trust Aggregation Portal



Supervisor:
Prof. Gihan Dias         128202E – A.G.E.A. Abeynayake
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
Problem Definition
• Numerous interconnections between individuals and
Organizations
      – 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
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
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
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
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.
Image courtesy: www.ftc.gov/bcp/workshops/.../personalDataEcosystem.pdf
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
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
Trust and Reputation
• Trust Classes [5] - According to Grandison &
  Sloman's 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
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
Trust and Reputation
• Algorithms and Methods for Measurement [7]
  [8]
  – Collaborative filtering and Collaborative
    Sanctioning
  – Bayesian Networks
  – Fuzzy Decision Trees
  – Boosting algorithms
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.
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.

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A trust aggregation portal

  • 1. A Trust Aggregation Portal Supervisor: Prof. Gihan Dias 128202E – A.G.E.A. Abeynayake
  • 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. Problem Definition • Numerous interconnections between individuals and Organizations – 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. 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. 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. 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. 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.
  • 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. 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. Trust and Reputation • Trust Classes [5] - According to Grandison & Sloman's 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. 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. Trust and Reputation • Algorithms and Methods for Measurement [7] [8] – Collaborative filtering and Collaborative Sanctioning – Bayesian Networks – Fuzzy Decision Trees – Boosting algorithms
  • 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. 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.