A Framework of Location Data Sharing for Balancing Anonymity and Utility

Abstract

Data sharing across collaborative mining can aid the community through analyses for decision-making tasks. While sharing personal data with a 3rdparty, data anonymization is a lawful obligation. Besides, preventing unauthorized access is another demand for storing data in a database. This paper proposes a framework of location data anonymization and data privacy over the database to ensure utility while data mining. For anonymization, upon the location data, it exploits dynamic geofencing, gridding, differential privacy, and zero-knowledge proof consecutively to perform the geographical analyses. Also, to securely store the data in the database, it encrypts data by elliptic curve cryptography. The anonymization framework produces grid-based circular coordinate boundaries, adds controlled noise, and proves the 3rdparty about its query results without telling any knowledge of the location (i.e., coordinates). Finally, the performance evaluation, analyses, comparisons, etc., using real data demonstrate that the proposed framework retains a better trade-off between the data utility and anonymity than the state-of-the-art works.