Towards Secure and Privacy-Preserving Social Web Services
Date/Time: September 10, 3:40 pm
Location: B29 Atanasoff Hall
Social web services such as Facebook, Twitter, and Yelp bring new benefits to many aspects of our lives. However, they are also plagued by both conventional and emerging threats to security and privacy. In this talk, we will discuss some fundamental security and privacy issues in social web services, covering both attacks and defenses. First, we will discuss the framework called SybilFrame that we developed to detect fake (Sybil) accounts in online social networks. The SybilFrame framework models a social network as a pairwise Markov Random Fields and uses Loopy Belief Propagation to infer the labels (i.e., benign or fake) of accounts. Second, we will demonstrate that diverse private information, including user identity, user attributes, relationships between users, and user interests, can be inferred from public data with high accuracies via machine learning techniques.
Neil Gong is an assistant professor in the Department of Electrical and Computer Engineering at the Iowa State University. He got a Ph.D in computer science from the University of California, Berkeley in May 2015, and a B.E. in computer science from the University of Science and Technology of China in 2010. He is broadly interested in cybersecurity, privacy, and their intersection with data science. In particular, he recently focuses on secure and privacy-preserving social web services, authentication, as well as security and privacy in Internet-of-Things.
His research has been widely covered by popular media such as WIRED, NPR, and Slashdot.