An Ultra-Lightweight Data-Aggregation Scheme with Deep Learning Security for Smart Grid

Abstract

Various smart meter data aggregation protocols have been developed in the literature to address the rising privacy threats against customers' energy consumption data. However, most of these protocols require a smart meter (installed at the consumer's end) to either maintain a secret key or to run an authenticated key establishment scheme for interacting with the aggregator. Both of these approaches create additional requirements for the system. To address this issue, this article first proposes a machine-learning-based ultra-light-weight data aggregation scheme for smart grids that does not require a secret key to be maintained for communicating with the aggregator. In particular, unlike existing data aggregation schemes, in the proposed data aggregation scheme, neither the server nor the smart meter needs to store any secret. Instead, for every round of data aggregation, each smart meter uses an embedded PUF for generating a unique random response for a given challenge. On the other hand, the server maintains a PUF model for each smart meter for producing the same random response. This unique secret key is used to ensure the privacy of the metering data. Next, we propose an optimized data aggregation scheme using collaborative learning to enhance the performance of the proposed scheme.