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Talk 1: Dr. Jennifer Golbeck
Title: Trust, User Profiling, and Privacy in Social Media
Abstract: Users share vast amounts of information about themselves in social media. Some is shared directly and other data can be inferred by learning over their language, behavior, and interactions. In this talk, I will present an overview of my work on computing trust in social networks and on profiling information about users based on their online behavior. I will follow that with some of our current work that integrates this and other techniques into applications for security, intelligence, and military applications.
For more information on Dr. Golbeck see link: http://www.cs.umd.edu/~golbeck/
Talk 2: Dr. Elaine (Runting) Shi
Title: Privacy-preserving Distributed Data Analysis
Abstract: We consider a privacy-preserving distributed data analysis scenario, where data is distributed among users (or organizations), and users may not wish to entrust their sensitive data to a centralized party such as a cloud service provider. Participating parties are mutually distrustful, and a subset of the parties may be compromised and colluding.
We prove new lower bounds on the utility of distributed data analysis satisfying information theoretic differential privacy. To circumvent such lower bounds in practice, we consider computational differential privacy. We demonstrate new constructions that satisfy computational differential privacy while achieving asymptotically smaller error. Our constructions also offer several properties that will be desirable in a practical deployment scenario, including support for periodic aggregation, no peer-to-peer communication, and fault tolerance.
For more information on Dr. Shi see link: http://www.cs.umd.edu/~elaine/