Data privacy in Digital Twin Network

Digital Twin (DT) as the name suggest, simply means a digital replica of a physical system or object. The technology behind this is novel and it is believed that this will shape the future in building reliable and efficient systems. DT is a disruptive technology which provides capabilities that allows mimicking of physicals systems virtually to enable you carry out extensive testing, simulation of various scenarios, forecasting, and optimizing system configurations. Due to the benefits it provides most especially in the industrial sector, stakeholders are investing heavily in the use of this new model to help improve performance, service quality and revenue. However, because it is made up of a combination of technologies such as cyber-physical systems, Industrial internet of things, edge computers, virtualization infrastructures, artificial intelligence and Big data, the union of all this technology and their interactions with the physical system in the real world, possesses a lot of security risk [1]. A review of the potential threats associated with Digital Twin Networks (DTN) shows that the Data privacy is one of the biggest security challenges that exist in the DT model. Adversaries with access to compromised servers within the DTN environment that contains intellectual properties (IP) and sensitive data, may extract this information. This could be for the purpose of cyber espionage, or identifying critical vulnerabilities in the DT environment i.e. zero-days that could be exploited. This research work is focused on identifying the best and effective solution to deal with data privacy risk in DTN from a governance and operation perspective.