ERQ: An Efficient Range Query Scheme Under Local Differential Privacy
Ellen Z. Zhang, Yunguo Guan, Rongxing Lu, Harry Zhang · 2023
Crowdsourcing has recently become a popular method of outsourcing tasks to many individuals in data-oriented applications. However, privacy is still a significant concern as crowdsourcing relies on individual responses. This paper focuses on range queries in crowd sourcing scenarios and proposes an efficient range query scheme, called ERQ, under Local Differential Privacy (LDP). ERQ is characterized by employing i) the accumulated encoding and perturbation techniques to protect the privacy of user data, and ii) the k-anonymity technique to conceal the real endpoints of a range query. Detailed security analysis shows that ERQ can achieve the desirable privacy requirements. In addition, performance evaluation also indicates ERQ is efficient in terms of low computational costs and communication overhead, and can achieve better accuracy while preserving privacy.