FPMRQ: Fully Privacy-Preserving Multidimensional Range Queries on Encrypted Data

• Published in: IEEE internet of things journal Vol. 11; no. 7; p. 1
• Main Authors: Wang, Wenli, Jia, Zhuliang, Xu, Mengfan, Li, Shundong
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Cloud computing; Communication; Cryptography; Cybersecurity; Data privacy; Data storage; Efficiency; encrypted data; Internet of Things; Multidimensional data; Multidimensional methods; multiple dimensions; Outsourcing; Privacy; Privacy-preserving; Protocols; Queries; range query; Servers
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2023.3334615

Multidimensional range queries are typical database operations used to retrieve data. With the development of cloud computing, outsourcing data storage and queries to a cloud server is an attractive choice for data owners; however, this choice involves well-known privacy issues. To preserve data privacy, data should be encrypted before they are outsourced to the cloud. Therefore, exploring multidimensional range queries on encrypted data has important theoretical and practical significance. Certain privacy-preserving schemes have been proposed to support multidimensional range queries on encrypted data. However, these schemes exhibit either poor privacy performance or poor computational or communication performance. This makes such schemes impractical for resource-constrained scenarios such as Internet of Things (IoT) environments. To improve security and efficiency in making them applicable to IoT environments, we propose lightweight secure vector comparison and secure double-blind protocols as building blocks to construct an efficient scheme, named the Fully Privacy-preserving Multidimensional Range Queries scheme (FPMRQ), and prove that FPMRQ can resist database reconstruction and query-recovery attacks. To improve communication efficiency, we adopt methods to pack multidimensional data into single-dimensional data and aggregate multiple data records into a single record of data. Finally, we conducted numerous experiments on real-world datasets to examine the efficiency of FPMRQ, and the experimental results show that FPMRQ significantly improves the computational efficiency (almost three orders of magnitude faster) and communication efficiency (at least 7.15× faster) in comparison with existing schemes with the same security level. These results demonstrate the practicality of the FPMRQ for resource-restrained environments, such as IoT.