FGAMD: A Flexible-Group-Based Privacy-Preserving Aggregation Scheme for Multidimensional Data in Edge-Enhanced IoT
Shuhang Xu, Jinmei Fan, Xiao Wei, Yanhai Zhang · IEEE Internet of Things Journal · 2025
With the rapid development of Internet of Things (IoT), a large amount of data is transmitted within systems, which sparks numerous applications through data processing and analysis. As an expected infrastructure of IoT, although edge computing improves the redundancy and cost of the system, security and privacy are still important research issues. In recent years, privacy-preserving data aggregation has attracted extensive research attention. Most existing schemes are based on asymmetric encryption and allow the transmission of encrypted individual data, which leads to high computation and communication costs and uncertainty for privacy. To address this, a flexible-group-based privacy-preserving aggregation scheme for multidimensional data in edge-enhanced IoT (FGAMD) is proposed in this article, which is based on symmetric encryption and Chinese remainder theorem (CRT). The homomorphic properties of the CRT are utilized to construct data aggregation groups for users, which reduces the communication burden of the gateway. Furthermore, by setting different thresholds of the aggregation groups, variable security and robustness requirements can be meet for flexible applications. Through analysis, FGAMD greatly reduces the computation cost, and due to the flexible terminal group size, the communication cost is significantly lower than that of similar strategies when the terminal group size is small.