Privacy-Aware Mobility Data Exploration

Abstract

The increasing availability of data due to the explosion of mobile devices and positioning technologies has led to the development of efficient mobility data management and mining techniques. However, the analysis of such data may enhance significant risks regarding individuals’ privacy. Consider for example a user requesting a service for nearby points of interest (POI), such as restaurants or pharmacies. Even if hiding user identifier, the request contains enough information to identify the requester. By linking exact coordinates sent to the service provider with public available information about POI’s, a third party can increase the probability that the request was sent e.g. from user’s home. Consequently, location data should be kept confidential since its disclosure may represent a brutal violation of privacy protection rights. Moreover, developing techniques able to analyze and extract significant patterns from traces left by moving objects can provide insight to the data holders and support to decision-making and strategic planning activities (consider, for instance, patterns depicting typical movement behavior of people moving in an urban environment and how these patterns evolve over time). For this reason, publishing mobility data for analysis purposes is an unavoidable need. But what kinds of privacy threats rise if a MOD is released? By linking an anonymous MOD with public available information, is a malevolent user able to conclude personal behaviors or, even worse, uniquely re-identify the user behind a trajectory? This chapter provides a survey regarding privacy-preservation techniques for location and moving object data. In particular, we discuss the challenges with respect to privacy on mobility data, focusing on three categories of privacy-preservation techniques, namely (a) privacy in the context of Location-based Services (LBS), where a trusted server aims at providing the service without threatening the anonymity of the user requiring the service, (b) privacy-preserving mobility data publishing, where the goal is to release a sanitized version of the original MOD for public use, and (c) privacy-aware mobility data querying, where the focus is on providing anonymous answers to queries posed by the users to a MOD that is maintained in-house.