Privacy preservation in outsourced mobility traces through compact data structures

Abstract

Indoor localization is widely used as enabling technology for location-based services, such as advertising, indoor routing, and behavioral analysis. To keep these features available, service providers passively collect a large amount of data that may reveal strictly personal information about an individual. As an example, a timestamped mobility trace acquired in a mall may help the business owner to rearrange the user surroundings relying on a punctual analysis of the user behavior. In this paper we discuss some information processing techniques relying on probabilistic data structures designed to mitigate the user’s privacy leakage. The work is also accompanied by a case study. Our experiments were carried out using well-known networking equipment, Cisco Meraki, which is provided in combination with several primitives designed to passively infer and collect the user position in an indoor environment.