Privacy-Aware and Hardware Acceleration-Based Aggregation Scheme for Smart Grid Networks

Document Type

Conference Proceeding

Publication Date



Computing Sciences and Computer Engineering


The smart grid revolutionizes power delivery through efficient and reliable two-way communication. Smart meters are used in Advanced Metering Infrastructure (AMI) networks to report electricity use in real-time to the utility company (UC), making AMI networks a crucial part of the smart grid. UC within the AMI network must access aggregated readings from several smart meters. However, if individual readings were accessed, it could reveal sensitive information about the user's privacy. This research presents a privacy-aware aggregation technique that uses the k-nearest neighbor encryption technique. The suggested technique aggregates encrypted bits instead of binary data and count the number of ones in each digit after decryption to protect sensitive customer information. The proposed scheme offers full and partial decryption to fulfill the preferences of getting total consumed power from all/one devices/appliances. As the number of smart meters within the AMI network will be huge and the data that needs to be aggregated will be significant, we propose hardware acceleration to implement our proposed aggregation scheme to fasten the process. The proposed method is implemented on Nexys A7-100T FPGA at a frequency of 100 MHz. Extensive security and privacy analyses confirm the scheme's effectiveness. Additionally, performance evaluations demonstrate that the scheme exhibits minimal communication and computation overheads. The results show the proposed method spends 0.074 ms and μS for encryption and aggregation time, respectively. Also, the proposed method uses small resources from the FPGA design.

Publication Title

2023 Eighth International Conference On Mobile and Secure Services