Enabling Privacy-Preserving Image-Centric Social Discovery

Host

Department of Computer Science

Cong Wang has been an Assistant Professor with the Department of Computer Science, City University of Hong Kong, Hong Kong, since August 2012. He received the B.E. and M.E. degrees from Wuhan University, Wuhan, China, and the Ph.D. degree from the Illinois Institute of Technology, Chicago, IL, USA, all in electrical and computer engineering. He interned with the Palo Alto Research Center, Palo Alto, CA, USA, in the summer of 2011. His research interests are in the areas of cloud computing security, with current focus on secure data outsourcing and secure computation outsourcing in public cloud. His work is currently supported by the Research Grants Council of Hong Kong under an Early Career Scheme. He is a member of IEEE and ACM.

Description

The increasing popularity of images at social media sites is posing new opportunities for social discovery applications, i.e., suggesting new friends and discovering new social groups with similar interests via exploring images. To effectively handle the explosive growth of images involved in social discovery, one common trend for many emerging social media sites is to leverage the commercial public cloud as their robust backend datacenter. While extremely convenient, directly exposing content-rich images and the related social discovery results to the public cloud also raises new acute privacy concerns. In light of this observation, in this talk we introduce our initial efforts in addressing these challenges through a privacy-preserving social discovery service architecture based on encrypted images. As the core of such social discovery is to compare and quantify similar images, we first adopt the effective Bag-of-Words model to extract the “visual similarity content” of users’ images into image profile vectors, and then model the problem as similarity retrieval of encrypted high-dimensional image profiles. To support fast and scalable similarity search over hundreds of thousands of encrypted images, we propose a secure and efficient indexing structure. The resulting design enables social media sites to obtain secure, practical, and accurate social discovery from the public cloud, without disclosing the encrypted image content. We show how to formally prove the security of the design and discuss further extensions on user image update. We also report experiment results on a large Flickr image dataset, with practical performance and qualitative social discovery results consistent with human perception.