Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications

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.

Privacy by Projection: Federated Population Density Estimation by Projecting on Random Features

“…A range of privacy-preserving techniques have been developed and added onto the basic federated learning framework, including differential privacy (DP) on the mobiles and/or the server [ 27 , 34 ], secure aggregation [ 7 , 25 ], etc. The state-of-the-art technique for adding carefully calibrating noise [ 13 , 40 ] is DP, including central DP [ 45 ] and local DP.…”

“…Unfortunately, even in a Federated KDE setting where users’ data is not directly disclosed, a malicious server can still infer users’ locations, by querying users for local density information and using it to deduce their most probable locations. A range of privacy-preserving techniques have been developed and added onto the basic federated learning and analytics frameworks, including differential privacy (DP) on the mobiles and/or the server [ 16 , 27 , 34 ], secure aggregation [ 7 , 15 ], and combinations thereof [ 25 ], [ 3 ].…”

“…Next, we explain the intuition of our proposed method and compare it to that of local noise-adding (including DP-based) privacy-preserving approaches for location-based applications [ 27 , 34 ]. To protect against a malicious server, most existing techniques conceal users’ locations by adding local noise (on the data and/or updates).…”

The Necessity of Close Contact Tracing in Combating COVID-19 Infection – A Systemic Study

“…Location-based tracing is used for identifying the groups or individuals that have been at an outbreak location at a particular time [13]. As dimensions of distance between person-to-person are not used, location-based tracing is not able to efficiently detect close contacts but rather utilizes the probability that these groups will be infected due to their close proximity to the scene [14]. In fulfilling the probability, data such as location, time, user ID, and many others are being collected.…”

Sensor Networks: Physical and Social Sensing in the IoT

“…The topical theme of user device location privacy is explored in the paper “Perturbed-Location Mechanism for Increased User-Location Privacy in Proximity Detection and Digital Contact-Tracing Applications” by Lohan et al [ 12 ], which presents perturbation-based location privacy protection, applied to location-based and proximity-based services (e.g., COVID-19 contact tracing). The approach is validated with simulation-based results in multi-floor building scenarios, enabling devices to adjust the accuracy level for location sharing with service providers.…”