Customized privacy preserving for inherent data and latent data

The huge amount of sensory data collected from mobile devices has offered great potentials to promote more significant services based on user data extracted from sensor readings. However, releasing user data could also seriously threaten user privacy. It is possible to directly collect sensitive information from released user data without user permissions. Furthermore, third party users can also infer sensitive information contained in released data in a latent manner by utilizing data mining techniques. In this paper, we formally define these two types of threats as inherent data privacy and latent data privacy and construct a data-sanitization strategy that can optimize the tradeoff between data utility and customized two types of privacy. The key novel idea lies that the developed strategy can combat against powerful third party users with broad knowledge about users and launching optimal inference attacks. We show that our strategy does not reduce the benefit brought by user data much, while sensitive information can still be protected. To the best of our knowledge, this is the first work that preserves both inherent data privacy and latent data privacy.