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Social Computing and Incentivized Sharing

Social Computing and Incentivized Sharing. Group Members. UT Dallas Murat Kantarcioglu Alain Bensoussan (UT Dallas) Nathan Berg Bhavani Thuraisingham University of Michigan Lada Adamic UMBC Yelena Yesha Joel Sachs Anupam Joshi Tim Finin UTSA Shouhuai Xu Ravi Sandhu Purdue

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Social Computing and Incentivized Sharing

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  1. Social Computing and Incentivized Sharing

  2. Group Members • UT Dallas • Murat Kantarcioglu • Alain Bensoussan (UT Dallas) • Nathan Berg • Bhavani Thuraisingham • University of Michigan • Lada Adamic • UMBC • Yelena Yesha • Joel Sachs • Anupam Joshi • Tim Finin • UTSA • Shouhuai Xu • Ravi Sandhu • Purdue • Elisa Bertino • Chris Clifton • Gabriel Ghinita • Ningui Li

  3. social networks & the AISL information dissemination and filtering incentivized sharing SOCIALNETWORKS privacy and security UT Dallas

  4. social networks and information sharing • distributing information • determining who should receive information • pulling information • information filtering (e.g. digg, FriendFeed) • securing information • propagation of trust and distrust (reputation management) • enhance trustworthiness of cryptographic key infrastructure • incentivizing sharing • individuals more or less likely to share with close contacts

  5. novelty relevance trust incentive for sharing information flow path individual receiving information acquiring: novelty and community structure • data quality/provenance • identify circular intelligence through proximity in social networks • temporal & source information can be mapped onto social network

  6. Knows Knowledge iN sharing & acquiring: open participation

  7. Knowledge In open participation: information or noise? ``Knowledge search is like oozing out knowledge in human brains to the Internet. People who know something better than others can present their know-how, skills or knowledge'' NHN CEO Chae Hwi-young “(It is) the next generation of search… (it) is a kind of collective brain -- a searchable database of everything everyone knows. It's a culture of generosity. The fundamental belief is that everyone knows something.” -- Eckart Walther (Yahoo Research)

  8. A-space

  9. Network analysis can be used to determine expertise Preferred Helper: ‘best available’ Preferred Helper: ‘just better’

  10. and study competitive information sharing • Java Forum: asker -> replier • Task CN: submitter -> winner

  11. structure of complex networks • heterogeneous connectivity and participation • robustness • connectivity • propagation of misinformation from compromised nodes

  12. Secure social networks: research goal • We ask and address the following two questions: • How should we exploit social computing/networks for security purposes (e.g., assured information sharing)? • How should we secure social computing/networks?

  13. Security by social computing • protect cryptographic security • detect and deter malicious players • trustworthy and survivable storage • reputation management • reliable and secure information dissemination

  14. Security for social computing • anonymous & accountable social computing • privacy-preserving social computing • inferring private data from social connections • secure incentive mechanisms • manipulation-resistant social computing • data quality/provenance • obscuring identity and connections

  15. prior work • Secure social networks & privacy: • Network robustness: (poster: Xu et al. “Generalized Epidemic Threshold, with Applications”) • Key signing: “Exploiting social networks for threshold signing” (AsiaCCS'08) • Privacy and trust in information sharing (Kantarcioglu UTDCS-21-08 ) • Information sharing & filtering: • Inferring expertise in Q&A forums (Adamic et al. WWW07&08) • Using social networks to detect conflicts of interest (Joshi et. al: WWW2006, ACM Tweb, 2008) • online information sharing structure (blogs,twitter) • Adamic (WI 2005,ICWSM07&08) • Finin, Joshi (WebKDD,ICWSM07&08,+ many more) • data quality and providence (detecting spam blogs) (AAAI 2006,ICWSM07,TREC2006) • Social and semantic computing: • blending the social & semantic (Sachs AAAI 07 workshop)

  16. current research • social networks as a filtering & aggregation tool • Yelena Yesha, Lada Adamic, Anupam Joshi, Tim Finin • understand the role of social networks (e.g. FriendFeed, blogs) in information diffusion • sentiment analysis and network structure • security for and from social networks • Shouhuai Xu, Ravi Sandhu • Understanding properties of social networks under attack • Using insights to make social computing more robust and secure • incentivized sharing in social networks • Murat Kantarcioglu, Lada Adamic • Experiments: effect of social connections on information sharing (peer recognition) and ability to recognize poor information sources • Social engineering (phishing, etc.)

  17. social networks & the AISL information dissemination and filtering incentivized sharing SOCIALNETWORKS privacy and security UT Dallas

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