Abstract
Future social networks will rely heavily on sensing data collected from users’ mobile and wearable devices. A crucial component of such sensing will be the full or partial access to user’s location data, in order to enable various location-based and proximity-detection-based services. A timely example of such applications is the digital contact tracing in the context of infectious-disease control and management. Other proximity-detection-based applications include social networking, finding nearby friends, optimized shopping, or finding fast a point-of-interest in a commuting hall. Location information can enable a myriad of new services, among which we have proximity-detection services. Addressing efficiently the location privacy threats remains a major challenge in proximity-detection architectures. In this paper, we propose a location-perturbation mechanism in multi-floor buildings which highly protects the user location, while preserving very good proximity-detection capabilities. The proposed mechanism relies on the assumption that the users have full control of their location information and are able to get some floor-map information when entering a building of interest from a remote service provider. In addition, we assume that the devices own the functionality to adjust to the desired level of accuracy at which the users disclose their location to the service provider. Detailed simulation-based results are provided, based on multi-floor building scenarios with hotspot regions, and the tradeoff between privacy and utility is thoroughly investigated.
Highlights
Introduction and Problem StatementPeople are increasingly interconnected through their wireless devices, such as smartphones, smartwatches, and other wearable devices
Proximity-detection services based on wireless signals, and in particular based on Bluetooth Low Energy (BLE), have gained a significant interest in the past two years as they are enabling digital contract-tracing techniques [4] shown to be relevant in the context of COVID-19 disease management [5,6]
As the user privacy is highly preserved with an argmax metric and high enough e values, the price to pay in terms of false alarm probabilities of up to 16% may seem reasonable for users desiring high location privacy
Summary
People are increasingly interconnected through their wireless devices, such as smartphones, smartwatches, and other wearable devices. In our paper we directly use the Euclidian distance between the true and perturbed locations as a measure of user location privacy and we study its tradeoff with the service utility Another location privacy-preserving approach in the literature, which is an adherent of Differential Privacy (DP), is the concept of the Private Spatial Decomposition presented in [19]. This paper proposes a new perturbation metric suitable for proximity-detection-based services and applications relying strictly on the relative distance between two users, but not needing absolute location information, offers a theoretical analysis of its properties, and demonstrates via extensive simulation-based results a very good tradeoff between privacy preservation and service utility.
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