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Abstract

The manufacturing sector is a vital component of most economies, which leads to a large number of 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.

Publication Type:	Conference or Workshop Item (Paper)
Additional Information:	This work is licensed under a Creative Commons Licence.
Publisher Keywords:	Data-sharing, Local Differential Privacy, Manufacturing, Privacy-preserving mechanism, Smart Utility.
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments:	School of Health & Psychological Sciences School of Health & Psychological Sciences > Psychology School of Science & Technology School of Science & Technology > Engineering
SWORD Depositor:	Symplectic Administrator

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