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Local differential privacy-based data-sharing scheme for smart utilities

The manufacturing sector is a vital component of most economies, which leads to many cyberattacks on organisations, whereas disruption in operation may lead to significant economic consequences. Adversaries aim to disrupt the production processes of manufacturing companies, gain financial advantages, and steal intellectual property by getting unauthorised access to sensitive data.

Access to sensitive data helps organisations to enhance the production and management processes. However, majority of the existing data-sharing mechanisms are either susceptible to different cyber-attacks or heavy in terms of computation overhead.

In this paper, a privacy-preserving data-sharing scheme for smart utilities is proposed. First, a customer’s privacy adjustment mechanism is proposed to make sure that end-users have control over their privacy, which is required by the latest government regulations, such as the General Data Protection Regulation.

Secondly, a local differential privacy-based mechanism is proposed to ensure privacy of the end-users by hiding real data based on the end-user preferences. The proposed scheme may be applied for different industrial control systems, whereas in this study, it is validated for energy utility use case consisting of smart intelligent devices. The results show that the proposed scheme may guarantee the required level of privacy with an expected relative error in utility.

This work was carried out by Veniamin Boiarkin, Dr. Bruno Bogaz Zarpelao, Professor Muttukrishnan Rajarajan, Professor Rajkumar Roy and Dr. Katy Tapper of City, University of London, United Kingdom and the State University of Londrina, Brazil.

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