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2013/03/20, MC103, 12 - 12:30

Confidentiality and Integrity Management in Online Systems
Amin Ranjbar , McGill

Abstract:

The dominant role of social networking in the web is turning human relations into conduits of information flow. This means that the way information spreads on the web is determined to a large extent by human decisions. Consequently, information security, confidentiality and integrity of shared data, lies on the quality of the collective decisions made by the users. Recently, many access control schemes have been proposed to control unauthorized propagation and modification of information in online systems; however, there is still a need for mechanisms to evaluate the risk of information leakage and unauthorized modifications within online systems. First, the thesis focuses on the confidentiality of information in online social networks. A novel community-centric confidentiality control mechanism for information flow management on the social web is presented. A Monte Carlo based algorithm is developed to determine the potential spread of a shared data object and to inform the user of the risk of information leakage associated with different sharing decisions she can make in a social network. The scheme also provides a facility to reduce information flowing to a specific user (i.e., black listing a specific user). Second the thesis focuses on the integrity of artifacts in crowdsourcing systems. A new approach for managing the integrity of contents created in crowdsourcing repositories named Social Integrity Management (SIM) is presented. SIM integrates two conflicting approaches to manage integrity in crowdsourcing systems: owner-centric and owner-free schemes. The ownership bottleneck is relaxed by including co-ownerships and having multiple versions. Finally, the thesis presents a thorough analysis of the Stack Exchange sites as an example of widely used crowdsourcing question answering systems. The dump datasets are used to analyze various user behaviors in crowdsourcing question answering systems by considering the effect of tagging, user reputation and user feedback. Observed characteristics from the studies are used in the modeling and evaluation of social integrity management.