Privometer: Privacy protection in social networks

The increasing popularity of social networks, such as Facebook and Orkut, has raised several privacy concerns. Traditional ways of safeguarding privacy of personal information by hiding sensitive attributes are no longer adequate. Research shows that probabilistic classification techniques can effectively infer such private information. The disclosed sensitive information of friends, group affiliations and even participation in activities, such as tagging and commenting, are considered background knowledge in this process. In this paper, we present a privacy protection tool, called Privometer, that measures the amount of sensitive information leakage in a user profile and suggests selfsanitization actions to regulate the amount of leakage. In contrast to previous research, where inference techniques use publicly available profile information, we consider an augmented model where a potentially malicious application installed in the user’s friend profiles can access substantially more information. In our model, merely hiding the sensitive information is not sufficient to protect the user privacy. We present an implementation of Privometer in Facebook

Record Linkage Based on Entities\u27 Behavior

Record linkage is the problem of identifying similar records across different data sources. Traditional record linkage techniques focus on using simple database attributes in a textual similarity comparison to decide on matched and non-matched records. Recently, record linkage techniques have considered useful extracted knowledge and domain information to help enhancing the matching accuracy. In this paper, we present a new technique for record linkage that is based on entity’s behavior, which can be extracted from a transaction log. In the matching process, we measure the improvement of identifying a behavior when comparing two entities by merging their transaction log. To do so, we use two matching phases; first, a candidate generation phase, which is fast and provide almost no false negatives, while producing low precision. Second, an accurate matching phase, which enhances the precision of the matching at high run time cost. In the candidates phase generation, behavior is represented by points in the complex plan, where we perform approximate evaluations. In the accurate matching phase, we use a heuristic called compressibility, where identified behaviors are more compressible. Our experiments show that the proposed technique can be used to enhance the record linkage quality while being practical for large logs. We also perform extensive sensitivity analysis for the technique’s accuracy and performance