A trust aggregation portal
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This document proposes a Trust Aggregation Portal that would aggregate trust and reputation data from various social networks and identity systems. It discusses problems with existing isolated systems and the need to collect and combine both overlapping and non-overlapping user profile and activity information. A literature review covers research on identity ecosystems, data collection methods, and existing trust and reputation models. The portal would provide a trust mark for individuals based on feedback, activities, and context to help address issues of trust across disconnected online systems.
![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](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-6-320.jpg)
![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.](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-7-320.jpg)
![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](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-9-320.jpg)
![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](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-10-320.jpg)
![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](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-11-320.jpg)
![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](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-12-320.jpg)
![Trust and Reputation
• Algorithms and Methods for Measurement [7]
[8]
– Collaborative filtering and Collaborative
Sanctioning
– Bayesian Networks
– Fuzzy Decision Trees
– Boosting algorithms](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-13-320.jpg)
![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.](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-14-320.jpg)
![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.](https://image.slidesharecdn.com/atrustaggregationportal-121124221632-phpapp02/85/A-trust-aggregation-portal-15-320.jpg)
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.