Biblio

The prevalent use of mobile applications using location information to improve the quality of their service has arisen privacy issues, particularly regarding the extraction of user's points on interest. Many studies in the literature focus on presenting algorithms that allow to protect the user of such applications. However, these solutions often require a high level of expertise to be understood and tuned properly. In this paper, the first control-based approach of this problem is presented. The protection algorithm is considered as the ``physical'' plant and its parameters as control signals that enable to guarantee privacy despite user's mobility pattern. The following of the paper presents the first control formulation of POI-related privacy measure, as well as dynamic modeling and a simple yet efficient PI control strategy. The evaluation using simulated mobility records shows the relevance and efficiency of the presented approach.

The widespread adoption of Location-Based Services (LBSs) has come with controversy about privacy. While leveraging location information leads to improving services through geo-contextualization, it rises privacy concerns as new knowledge can be inferred from location records, such as home/work places, habits or religious beliefs. To overcome this problem, several Location Privacy Protection Mechanisms (LPPMs) have been proposed in the literature these last years. However, every mechanism comes with its own configuration parameters that directly impact the privacy guarantees and the resulting utility of protected data. In this context, it can be difficult for a non-expert system designer to choose appropriate configuration parameters to use according to the expected privacy and utility. In this paper, we present a framework enabling the easy configuration of LPPMs. To achieve that, our framework performs an offline, in-depth automated analysis of LPPMs to provide the formal relationship between their configuration parameters and both privacy and the utility metrics. This framework is modular: by using different metrics, a system designer is able to fine-tune her LPPM according to her expected privacy and utility guarantees (i.e., the guarantee itself and the level of this guarantee). To illustrate the capability of our framework, we analyse Geo-Indistinguishability (a well known differentially private LPPM) and we provide the formal relationship between its &epsis; configuration parameter and two privacy and utility metrics.