Dual Scheme Privacy-Preserving Approach for Location-Aware Application in Edge Computing

Bruce Gu, Youyang Qu, Khandakar Ahmed, Wenjie Ye, Chenchen Tan, Yuan Miao

Abstract: The location awareness capabilities of edge computing (EC) contains large quantity of the physical devices with short coverage range. The possibilities of the potential private data attacks from adversaries increases dramatically through easily accessible location information. The existing research on privacy-preserving schemes cannot meet various privacy-preserving expectations in practice for EC variants. In this paper, we proposed a dual scheme customizable \(\epsilon \)-differential privacy preservation to provide comprehensive protection. We establish the first scheme by clustering Edge Nodes (ENs) with SDN-enabled EC where SDN enables the capabilities of the programmability. In addition, we customize the \(\epsilon \)-differential privacy preservation scheme for variant EC services with the employment of modified Laplacian mechanism to generate noise, where the optimal tradeoff been found. The extensive experiments results demonstrate the significance of the proposed model in terms of privacy protection level and data utility, respectively.