Privacy-Preserving Data Aggregation

Framework for Mobile Service

Based Multiuser Collaboration

 Hai Liu¹, Zhenqiang Wu¹, Changgen Peng², Feng Tian¹, and Laifeng Lu³

¹School of Computer Science, Shaanxi Normal University, China

²Guizhou Provincial Key Laboratory of Public Big Data, Guizhou University, China

³School of Mathematics and Information Science, Shaanxi Normal University, China

Abstract: Considering the untrusted server, differential privacy and local differential privacy has been used for privacy-preserving in data aggregation. Through our analysis, differential privacy and local differential privacy cannot achieve Nash equilibrium between privacy and utility for mobile service based multiuser collaboration, which is multiuser negotiating a desired privacy budget in a collaborative manner for privacy-preserving. To this end, we proposed a Privacy-Preserving Data Aggregation Framework (PPDAF) that reached Nash equilibrium between privacy and utility. Firstly, we presented an adaptive Gaussian mechanism satisfying Nash equilibrium between privacy and utility by multiplying expected utility factor with conditional filtering noise under expected privacy budget. Secondly, we constructed PPDAF using adaptive Gaussian mechanism based on negotiating privacy budget with heuristic obfuscation. Finally, our theoretical analysis and experimental evaluation showed that the PPDAF could achieve Nash equilibrium between privacy and utility. Furthermore, this framework can be extended to engineering instances in a data aggregation setting.

Keywords: Differential privacy, Nash equilibrium, conditional filtering noise, adaptive Gaussian mechanism, PPDAF.

Received November 22, 2017; accepted October 4, 2018