A Blockchain-Enabled Location Privacy-preserving under Local Differential Privacy for Internet of Vehicles

Abstract

Location and user information can be shared and interacted in the Internet of Vehicles (IoV), which bring many benefits to drivers and consumers. However, private issues become more acute as their data is outsourced to third parties. It is easy for sensitive information to be leaked in a big data environment. To solve these problems, a location data algorithm that satisfies Local Differential Privacy (LDP) is proposed to protect user privacy. In this paper, we use the randomized response mechanism to reconstruct the Laplace algorithm so that it satisfies LDP, perturbing the original location of each user from the client. The user location is clustered using k-means clustering algorithm, the perturbed data are noise reduced in the blockchain software development kit (SDK), and the noise reduced location data is uploaded to the blockchain network for storage through smart contracts. In addition, the effectiveness of the privacy protection mechanism is verified by comparative experiments. Compared with the existing privacy protection methods, the privacy protection mechanism not only can meet the privacy needs of users better, but also the noise reduction algorithm in the SDK can restore the original data and has higher data availability.