EAGER: TWC: Collaborative: iPrivacy: Automatic Recommendation of Personalized Privacy Settings for Image Sharing

The objective of this project is to investigate a comprehensive image privacy recommendation system, called iPrivacy (image Privacy), which can efficiently and automatically generate proper privacy settings for newly shared photos that also considers consensus of multiple parties appearing in the same photo. Photo sharing has become very popular with the growing ubiquity of smartphones and other mobile devices. However, many people especially young users of social networks often share private photos about themselves and their friends without being aware of the potential impact on their future lives caused by unwanted disclosure and privacy violations. Although some photo sharing platforms start to offering functions of privacy configuration, such manual process could be very tedious for users and also error-prone since not many users have sufficient background knowledge about privacy. This project will address these rising privacy concerns of photo sharing in social sites and benefit billions of social network users. The broader impact of this project will be further enhanced by the integration of education and research. A range of educational activities will be carried out including curriculum development, professional training for students and cybersecurity camp for K-12 teachers, with emphasis to under-represented groups.

This project will seamlessly integrate expertise from two different domains: image understanding and privacy management, leading to one of the first comprehensive and automatic policy recommendation systems. The proposed project contains the following innovative researches. First, a multi-party privacy-sensitive object identification algorithm will be developed which will be capable of automatically generating the identity of each human subject in a photo so as to automate the subsequent privacy harmonization process. Second, a unique privacy harmonization approach will be designed, which will conduct hierarchical privacy policy mining to understand different levels of privacy concerns in communities, recommend policies that effectively harmonize privacy preferences of multiple people appearing in the same photo and also adapt to the evolution of people's privacy preferences. The proposed iPrivacy system will not only fully release the burden of privacy configuration at users' side, but will also promote better privacy practice based on knowledge learned from large-scale historical and societal information.