An Online Framework for Publishing Dynamic Privacy-Sensitive Location Traces

This paper considers the problem of protecting individual anonymity when continuously publishing a stream of location trace information collected from a population of users. Fundamentally, the key challenge that arises in this setting is the presence of evolving data, and in particular, data that evolves in semi-predictable ways. The main contribution of this paper is the first comprehensive formal framework for reasoning about privacy in this setting. Through careful analysis of the expected threat, we articulate a new privacy principle called temporal unlinkability. Then, by incorporating a model of user motion, we are able to quantify the risk of privacy violations probabilistically. Within this framework, we develop a simple initial set of algorithms for continuous publishing, and we demonstrate the feasibility of the approach using both real and synthetic location data.