Cyber-Resilient Privacy Preservation and Secure Billing Approach for Smart Energy Metering Devices

TL;DR

This paper proposes a novel deep learning-based framework to enhance privacy preservation and security in smart energy metering devices, addressing existing limitations of computational overload and vulnerability to attacks.

Contribution

It introduces a new privacy and security framework leveraging deep learning to improve cyber resilience and reduce computational burdens on smart energy meters.

Findings

Enhanced privacy protection against malicious attacks

Reduced computational and communication overhead

Improved robustness of smart energy metering systems

Abstract

Most of the smart applications, such as smart energy metering devices, demand strong privacy preservation to strengthen data privacy. However, it is difficult to protect the privacy of the smart device data, especially on the client side. It is mainly because payment for billing is computed by the server deployed at the client's side, and it is highly challenging to prevent the leakage of client's information to unauthorised users. Various researchers have discussed this problem and have proposed different privacy preservation techniques. Conventional techniques suffer from the problem of high computational and communication overload on the client side. In addition, the performance of these techniques deteriorates due to computational complexity and their inability to handle the security of large-scale data. Due to these limitations, it becomes easy for the attackers to introduce malicious attacks on the server, posing a significant threat to data security. In this context, this proposal intends to design novel privacy preservation and secure billing framework using deep learning techniques to ensure data security in smart energy metering devices. This research aims to overcome the limitations of the existing techniques to achieve robust privacy preservation in smart devices and increase the cyber resilience of these devices.