A Privacy Preserving Framework for Rewarding Users in Opportunistic Mobile Crowdsensing

Crowdsensing is rapidly becoming an interesting approach for scenarios in which a significant amount of data is needed and a static infrastructure is not a viable option due to cost or other challenges. Although users collect data without any direct cost, it is common to reward them depending on the amount and quality of the data they provide. However, as this data also carries sensitive geolocation information, it also exposes the users to privacy concerns, if such data is accessed by a malicious entity. Geolocation information can disclose information about the habit of the user and his or her places of interest, however, in many cases, such information is crucial for the purpose of the application and cannot be omitted nor distorted. In this work, we present a novel framework for opportunistic MCS scenarios focused on maintaining the privacy of the users while rewarding them for their collected and geolocated data. We evaluate our proposal on real datasets, quantifying its benefits over other methodologies.