Adaptive Location Privacy with ALP

Abstract

With the increasing amount of mobility data being collected on a daily basis by location-based services (LBSs) comes a new range of threats for users, related to the over-sharing of their location information. To deal with this issue, several location privacy protection mechanisms (LPPMs) have been proposed in the past years. However, each of these mechanisms comes with different configuration parameters that have a direct impact both on the privacy guarantees offered to the users and on the resulting utility of the protected data. In this context, it can be difficult for non-expert system designers to choose the appropriate configuration to use. Moreover, these mechanisms are generally configured once for all, which results in the same configuration for every protected piece of information. However, not all users have the same behaviour, and even the behaviour of a single user is likely to change over time. To address this issue, we present in this paper ALP, a new framework enabling the dynamic configuration of LPPMs. ALP can be used in two scenarios: (1) offline, where ALP enables a system designer to choose and automatically tune the most appropriate LPPM for the protection of a given dataset; (2) online, where ALP enables the user of a crowd sensing application to protect consecutive batches of her geolocated data by automatically tuning an existing LPPM to fulfil a set of privacy and utility objectives. We evaluate ALP on both scenarios with two real-life mobility datasets and two state-of-the-art LPPMs. Our experiments show that the adaptive LPPM configurations found by ALP outperform both in terms of privacy and utility a set of static configurations manually fixed by a system designer.

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

“…Indeed, all LPPMs configuration mechanisms that we are aware of use greedy processes that need to run many experiments in order to converge to a suitable configuration (if ever they converge). We compare our framework PULP to the closest work from the state of the art, the configurator ALP from [26]. ALP is a framework that iteratively look for a LPPM configuration that satisfies high level objectives objectives such as maximizing privacy and utility.…”

“…In [8], the authors adapt the configuration of GEO-I to the density of the surrounding area, assuming that the less noise is needed to add to the original data for privacy protection when there are people around. In [26], the authors propose an iterative greedy mechanism that evaluates privacy and utility of obfuscated data to refine the parameters of a certain LPPM configuration. These solutions are often computing intensive as they are heuristics-based.…”

The Long Road to Computational Location Privacy: A Survey

“…Recent works have been conducted to select and configure the best LPPM to use according to a set of objectives set by the user in term of utility and privacy. For instance, ALP [81] relies on a greedy approach that iteratively evaluates the privacy and utility, thus refining the values of configuration parameters at each step. While ALP can be used for online and offline LPPMs, PULP [82] proposes a framework allowing to automatically choose and configure offline LPPMs.…”

“…Dynamically adapting the offered privacy level avoids over protecting data, and consequently provides a better utility. This has been explored by some previous works (e.g, [106], [122], [55], [81]). However, few of these works take into account the semantics of visited places.…”

Localisation based on Wi-Fi fingerprints: A crowdsensing approach with a device-to-device aim

“…This type of communications can also be envisaged as a technique to preserve privacy while collecting data. One of the main drawbacks of most location-based services is the revelation of points of interests (POI) of the users [2]. Indeed, since GPS is energy-consuming and provides a localisation after a few tens of seconds with a cold start, several techniques of localisations based on association of locations with ambient fingerprints have been proposed [3].…”