Preserving Privacy in Social Networking Systems: Policy-Based Control and Anonymity
Social Networking Systems (SNSs), such as Facebook, are complex information systems involving vast number of active entities (including users, applications, and third parties) that provide and consume overwhelming amount of information. Such information is mainly related or can be attributed to the users of SNSs and hence, can be considered privacy-sensitive. In this talk, I will present my research on development of models and methodologies to preserve user privacy in these systems. In the first part, I will present an ontology-based privacy control framework for SNSs that enables sharing policy authority with users and allowing them to have fine-grained control over their related information. I will then discuss how such an ontology-based foundation can be leveraged for formal policy analysis. In particular, I provide an approach to reason about the completeness of a user's control in an SNS. In the second part of the talk, I will present how concepts from social network analysis theory can be leveraged to enhance k-anonymization algorithms for social network datasets in terms of preserving structural properties of the networks.
Amirreza Masoumzadeh is a PhD candidate in Information Science at the University of Pittsburgh. His research interests span the broad areas of information security, privacy, and trust. His PhD dissertation is focused on developing models and methodologies to preserve user privacy in social networking environments. He is the recipient of the University of Pittsburgh iSchool’s Catherine Ofiesh Orner Award, and a MODAP best student paper award.