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Most of the smart applications, such as smart energy metering devices, demandstrong privacy preservation to strengthen data privacy. However, it isdifficult to protect the privacy of the smart device data, especially on theclient side. It is mainly because payment for billing is computed by the serverdeployed at the client's side, and it is highly challenging to prevent theleakage of client's information to unauthorised users. Various researchers havediscussed this problem and have proposed different privacy preservationtechniques. Conventional techniques suffer from the problem of highcomputational and communication overload on the client side. In addition, theperformance of these techniques deteriorates due to computational complexityand their inability to handle the security of large-scale data. Due to theselimitations, it becomes easy for the attackers to introduce malicious attackson the server, posing a significant threat to data security. In this context,this proposal intends to design novel privacy preservation and secure billingframework using deep learning techniques to ensure data security in smartenergy metering devices. This research aims to overcome the limitations of theexisting techniques to achieve robust privacy preservation in smart devices andincrease the cyber resilience of these devices.

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