Towards Privacy-Preserving and Practical Image-Centric Social Discovery

Images are becoming one of the key enablers of user connectivity in social media applications. Many of them are directly exploring image content to suggest new friends with similar interests. To handle the explosive volumes of images, one common trend is to leverage the public cloud as their robust service backend. Despite the convenience, exposing content-rich images to the cloud inevitably raises acute privacy concerns. In this paper, we propose a privacy-preserving architecture for image-centric social discovery services, designed to function over encrypted images. We first adopt the effective Bag-of-Words model to extract the “visual content” of users' images into respective image profile vectors. We then model the core problem as similarity retrieval of encrypted high-dimensional vectors. To achieve scalable services over millions of encrypted images, we design a secure and efficient index structure, which enables practical and accurate social discovery from the cloud, without revealing any image profile or image content. For completeness, we further enrich our service with secure updates, facilitating user's image update. Our implementation is deployed at an Android phone and Amazon Cloud, and extensive experiments are conducted on a large Flickr image dataset which demonstrates the desired quality of services.