Automatic Privacy and Utility Preservation for Mobility Data: A Nonlinear Model-Based Approach

Abstract

The widespread use of mobile devices and locationbased services has generated massive amounts of mobility databases. While processing these data is highly valuable, privacy issues can occur if personal information is revealed. The prior art has investigated ways to protect mobility data by providing a large range of Location Privacy Protection Mechanisms (LPPMs). However, the privacy level of the protected data significantly varies depending on the protection mechanism used, its configuration and on the characteristics of the mobility data. Meanwhile, the protected data still needs to enable some useful processing. To tackle these issues, we present PULP, a framework that finds the suitable protection mechanism and automatically configures it for each user in order to achieve user-defined objectives in terms of both privacy and utility. PULP uses nonlinear models to capture the impact of each LPPM on data privacy and utility levels. Evaluation of our framework is carried out with two protection mechanisms of the literature and four realworld mobility datasets. Results show the efficiency of PULP, its robustness and adaptability. Comparisons between LPPMs' configurator and the state of the art further illustrate that PULP better realizes users' objectives and its computations time is in orders of magnitude faster.

Privacy protection control for mobile apps users

“…The location privacy literature proposes few works on the protection apps configuration challenge, most of them treating the problem as a static one-i.e., working on already collected databases, possibly of temporally uncorrelated locations-and able either to work on a specific mechanism [14,3] or to choose between several [42,10]. L2P2 [51] is a dynamic objective-driven protection configuration law for location privacy.…”

“…By tuning those parameters we change the protection action. This property is highly valuable considering that privacy often comes at the cost of a reduction of the service utility: a configurable mechanism which allows to leverage the privacy to utility trade-off [10]. In particular, optimal privacy and utility protection cannot be universal [8], motivating the need for dynamic adaptation of the protection.…”