User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks

Farid Yessoufou, Salma Sassi, Elie Chicha, Richard Chbeir () and Jules Degila
Additional contact information
Farid Yessoufou: Department of Computer Science, IMSP, University of Abomey Calavi, 01, Abomey-Calavi P.O. Box 526, Benin
Salma Sassi: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Elie Chicha: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Richard Chbeir: Department of Computer Science, E2S UPPA, LIUPPA, University Pau & Pays Adour, 64600 Anglet, France
Jules Degila: Department of Computer Science, IMSP, University of Abomey Calavi, 01, Abomey-Calavi P.O. Box 526, Benin

Future Internet, 2024, vol. 16, issue 9, 1-29

Abstract: With the rise of the Internet of Things (IoT), mobile crowdsourcing has become a leading application, leveraging the ubiquitous presence of smartphone users to collect and process data. Spatial crowdsourcing, which assigns tasks based on users’ geographic locations, has proven to be particularly innovative. However, this trend raises significant privacy concerns, particularly regarding the precise geographic data required by these crowdsourcing platforms. Traditional methods, such as dummy locations, spatial cloaking, differential privacy, k-anonymity, and encryption, often fail to mitigate the risks associated with the continuous disclosure of location data. An unauthorized entity could access these data and infer personal information about individuals, such as their home address, workplace, religion, or political affiliations, thus constituting a privacy violation. In this paper, we propose a user mobility model designed to enhance location privacy protection by accurately identifying Points of Interest (POIs) and countering inference attacks. Our main contribution here focuses on user mobility modeling and the introduction of an advanced algorithm for precise POI identification. We evaluate our contributions using GPS data collected from 10 volunteers over a period of 3 months. The results show that our mobility model delivers significant performance and that our POI extraction algorithm outperforms existing approaches.

Keywords: location protection; personal data preservation; security breach; crowdsourcing; geolocated data; privacy; inference attack (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2024
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