Privacy preservation for spatio-temporal data in Mobile Crowdsensing scenarios

Abstract

Mobile Crowdsensing has become an important paradigm in the last decade for on-demand monitoring scenarios in Smart Cities and vehicular networks, when the deployment of a dedicated sensor network is no longer affordable. To foster the participation of a large user base, it is common to reward them on top of the amount and the quality of data provided. Regardless of the MCS policy adopted, this requires the crowdsourcer to keep track of the participants. Since the contributed data inherently carries sensitive spatio-temporal information, privacy problems arise if a malicious entity gains access to it; still, in some cases, the spatio-temporal precision is crucial for the benefit of the application and cannot be distorted. In this paper we propose a privacy preserving framework for opportunistic MCS scenarios that includes data collection and rewarding phases. The framework both retains the precision of spatio-temporal information and limits the sensitivity of information disclosed through an algorithm that clusters the data points into low correlated sets. The framework is agnostic about how correlation is calculated, and we propose three exemplary correlation functions. We evaluate our framework against six real world datasets, assessing its efficacy and envisioning its implementation in practical deployments.