An Efficient Bloom Filter-based Range Query Scheme Under Local Differential Privacy

While crowdsourcing for data collection has become increasingly popular in data-driven applications, privacy remains a significant challenge. This paper presents an effective scheme for conducting range queries under local differential privacy (LDP) in crowdsourcing applications, which addresses the privacy challenges that arise in such scenarios. In particular, our proposed scheme utilizes Prefix Encoding (PE) and Bloom Filter (BF) techniques to convert a large domain into a binary domain for improved query accuracy. When responding to the query, individual users can check a Bloom filter to determine whether their private item is within the query range and use the Basic Randomized Response (BRR) technique to perturb their result for achieving e-LDP. Detailed security analysis shows that our proposed scheme can preserve user's item privacy and also keep an external passive attacker from learning the query range. In addition, performance evaluation shows that the proposed scheme is efficient in terms of computational cost and communication overhead, while effectively balancing range query accuracy and privacy.