Title: An effective differential privacy protection method of location data based on perturbation loss constraint

Authors: Haiyan Kang; Ying Li; Shasha Zhang

Addresses: School of Information and Management, Beijing Information Science and Technology University, Beijing, Haidian District, China ' School of Information and Management, Beijing Information Science and Technology University, Beijing, Haidian District, China ' School of Computer Science, Beijing Information Science and Technology University, Beijing, Haidian District, China

Abstract: Differential privacy is usually applied to location privacy protection scenarios, which confuses real data by adding interference noise to location points to achieve the purpose of protecting privacy. However, this method can result in a significant amount of redundant noisy data and impact the accuracy of the location. Considering the security and practicability of location data, an effective differential privacy protection method of location data based on perturbation loss constraint is proposed. After applying the Laplace mechanism under the condition of differential privacy to perturb the location data, the Savitzky-Golay filtering technology is used to correct the data with noise, and the data with large deviation and low availability is optimised. The introduction of Savitzky-Golay filtering mechanism in differential privacy can reduce the error caused by noise data while protecting user privacy. The experiments results indicate that the scheme improves the practicability of location data and is feasible.

Keywords: location data; location-based service; location privacy; differential privacy; Savitzky-Golay filter.

DOI: 10.1504/IJIPT.2023.139344

International Journal of Internet Protocol Technology, 2023 Vol.16 No.4, pp.196 - 203

Received: 09 Apr 2023
Accepted: 03 Jun 2023
Published online: 01 Jul 2024 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article