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It is generally recognized that protecting online privacy is important, with modern society manifesting this concern in many ways. Preliminary research indicates that third parties, with modest crawling and computational resources, and employing simple data mining heuristics, can potentially combine online services and publicly available information to create detailed profiles of the users living in any targeted geographical area.
This research investigates measures that can significantly improve privacy protection of users, while not degrading their overall Internet experience. The focus is on less-trustworthy third parties (e.g., data brokers, advertisers, spammers, malware distributors, and pedophiles), who can scrape, aggregate and infer information from many different online and offline sources. This research has two interrelated research thrusts. First, it explores to what extent third parties can collect, aggregate, and statistically process information from OSNs and other online and offline sources to create profiles. This thrust is developing rigorous statistical methodologies and probabilistic models for estimating the degree of potential privacy leakage. Second, this research investigates a variety of privacy policies that governments can establish, and a wide range of measures OSNs can take, to reduce the privacy risk. For promising combinations of policies and measures, this research quantifies the trade-off between privacy protection and usability.
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TWC SBE: Small: Protecting the Online Privacy of Users of Social Networks