Multi-Functional Homomorphic Encryption Method Based on Crowd Sensing Networks

作     者：Chen, Hui Mou, Xingyu Wang, Zixuan Wu, Tianting Wang, Xin Wang, Chengcheng Song, Ruixiang Song, Lifang Jiang, Xiaoling Zhang, Xinjing Li, Yunfeng 

作者机构：Jilin Prov Meteorol Informat Network Ctr Changchun 130062 Jilin Peoples R China Changchun Meteorol Informat Technol Secur Ctr Changchun 130012 Jilin Peoples R China Jilin Prov Forestry Informat Ctr Changchun 130022 Jilin Peoples R China Jilin Prov Meteorol Serv Ctr Changchun 130062 Jilin Peoples R China Jilin Prov Meteorol Disaster Prevent Technol Ctr Changchun 130062 Jilin Peoples R China 

出 版 物：《IEEE ACCESS》 (IEEE Access)

年 卷 期：2025年第13卷

页      面：36795-36803页

核心收录：

基　　金：Jilin Province Science and Technology Development Plan Project [20230203134SF] China Meteorological Administration Public Weather Service Centre Innovation Fund [M2020013] 

主　　题：Sensors Data privacy Homomorphic encryption Protection Privacy Public key Differential privacy Data models Computational modeling Computational efficiency Crowd sensing networks multi-functional homomorphic encryption indistinguishability obfuscation puncturable pseudorandom 

摘      要：In response to the security and privacy issues associated with sensing devices in contemporary crowd sensing systems, the paper proposes a crowd sensing networks method based on the MFHE (the Multi-Functional Homomorphic Encryption) architecture. This method leverages fully homomorphic encryption technology, thereby eliminating the use of plaintext throughout the sensing process and enhancing security compared to traditional encryption methods. Initially, a sensing model is constructed based on the classic crowd sensing architecture. Subsequently, fully homomorphic encryption technology is employed for plaintext-free data transmission. The encryption scheme is developed on the MFHE architecture, with additional improvements introduced through indistinguishability obfuscation and puncturable pseudorandom function methods, which serve to shorten the ciphertext and reduce complexity, rendering it more suitable for crowd sensing environments. Finally, experiments conducted on real-world datasets demonstrate that the proposed method is effective in terms of both privacy protection and time efficiency.