Date of Award


Degree Type


Degree Name

Doctor of Philosophy in Electrical Engineering


Electrical, Computer, and Biomedical Engineering

First Advisor

Yan (Lindsay) Sun


Web 2.0 has been growing rapidly in the past decade, and leading to surging popularity of online social media. There are over 2.1 billion people that are using social media, which is 28% of the global population. Social media has become one of the most complex computing and communication systems in the planet. Social media attracts large amount of people to create, share and exchange information, interests, ideas, pictures, videos, and etc. in the virtual communities. In social media, people can interact with acquaintances and strangers, and thus privacy and security should be considered seriously.

From the privacy perspective, one of the severe type of privacy breach is related to online social networks, such as Facebook, Linkedin, Google+, and Twitter. Online social network users are often not aware of the size and the nature of the audience viewing their profiles, and therefore they may reveal more information than what is appropriate to be viewed publicly. Due to the lack of privacy awareness, online social network users can suffer a number of privacy related threats. In this dissertation, a quantitative online social network privacy risk analysis framework - TAPE is proposed. Inspired by the reliability analysis of a wireless sensor network, the binary decision diagram tool is employed to calculate online social network privacy level. The privacy awareness and privacy trust metrics are proposed to evaluate online social network users' intention of privacy protection. To our best knowledge, TAPE framework is the first work that take both privacy awareness and privacy trust into consideration. Based on the TAPE framework, we also propose an unfriending strategy in terms of privacy protection, which outperforms other existing unfriending strategies. The detail of this framework is introduced in Chapter 2.

From the security perspective, online product/service review system is one of the most vulnerable systems in social media. Since there are enormous profits of online markets and the customers' purchasing decision is relying on the product/service review, it is highly possible that firms and retailers at the online marketplace may create fake reviews to mislead customers. In this dissertation, a novel angle of fake review detection is introduced, which is called Equal Rating Opportunity (ERO) principle. Based on ERO principle, ERO analysis is proposed. ERO analysis can be implemented with limited cost. It is a new direction of fake review detection. Based on real data testing, ERO analysis is able detect new perspectives of fake review, which cannot be detected by other approaches, while giving a relatively low false alarm rate. The ERO principle and ERO analysis is presented in Chapter 3.



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