MPP-MDA: Multifunctional Privacy-Preserving Multisubset Data Aggregation for AMI Networks

Dan Sun, Shuai Zhao, Wanqiong Tao, Mianxue Gu, Jianhong Lin, Song Han

Abstract: The advanced metering infrastructure (AMI) network allows control center (CC) to collect residential users’ fine-grained electricity usage data every few minutes for energy management and real-time load monitoring. However, these fine-grained data may reveal users’ daily activities which raises serious privacy concerns. For allowing the CC to receive only the total electricity usage of users while preserve their privacy, many privacy-preserving data aggregation (PPDA) schemes have been put forward. Nevertheless, most of them have no regard for privacy-preserving multisubset data aggregation (PPMDA), where the CC not only needs to learn the number of users whose electricity usage lies within a given range but also the overall electricity usage of these users. Moreover, to the best of our knowledge, there is no formal study on achieving multifunctional PPMDA for AMI networks. In this article, we come up with a multifunctional, flexible, privacy-enhanced, and efficient PPMDA scheme, named MPP-MDA. In our MPP-MDA, the CC can compute multiple statistical function aggregations of each subset of users to provide various fine-grained services. In addition, for better flexibility, MPP-MDA supports billing of dynamic pricing, achieves fault tolerance and adapts to dynamic users. Moreover, MPP-MDA preserves differential privacy against differential attack, guarantees authentication and data integrity. Finally, MPP-MDA supports privacy-preserving fivefold-functional aggregation, which is able to reduce the computation and communication overheads significantly. The security discussion elaborates that MPP-MDA is secure against many attacks. The performance evaluation demonstrates that MPP-MDA has less computation and communication overheads.

External IDs:dblp:journals/iotj/SunZTGLH24