Learning Sparse Privacy-Preserving Representations for Smart Meters Data

TL;DR

This paper introduces a novel privacy-preserving method for smart meter data using non-uniform down-sampling with neural networks, achieving better privacy-utility balance and data efficiency compared to existing techniques.

Contribution

It proposes a neural network-based sparse representation approach for privacy-preserving smart meter data, improving privacy-utility trade-offs and data efficiency over prior methods.

Findings

Outperforms uniform and random down-sampling in privacy-utility trade-off.

Releases significantly less data while maintaining utility.

More efficient than state-of-the-art data manipulation methods.

Abstract

Fine-grained Smart Meters (SMs) data recording and communication has enabled several features of Smart Grids (SGs) such as power quality monitoring, load forecasting, fault detection, and so on. In addition, it has benefited the users by giving them more control over their electricity consumption. However, it is well-known that it also discloses sensitive information about the users, i.e., an attacker can infer users' private information by analyzing the SMs data. In this study, we propose a privacy-preserving approach based on non-uniform down-sampling of SMs data. We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility. The architecture is composed of a releaser, which is a recurrent neural network (RNN), that is trained to generate the sparse representation by masking the SMs data, and an utility and adversary networks (also RNNs), which help the releaser to minimize the leakage of information about the private attribute, while keeping the reconstruction error of the SMs data minimum (i.e., maximum utility). The performance of the proposed technique is assessed based on actual SMs data and compared with uniform down-sampling, random (non-uniform) down-sampling, as well as the state-of-the-art in privacy-preserving methods using a data manipulation approach. It is shown that our method performs better in terms of the privacy-utility trade-off while releasing much less data, thus also being more efficient.