Data privacy in construction industry by privacy-preserving data mining (PPDM) approach

Abstract

The architectural, engineering and construction (AEC) requires most efficient joint efforts between the construction project stakeholders with continuing exchange of large amount of project data. Nowadays, it is seen paradigm shift in construction industry to work on digital technologies like BIM from conventional methods of paper-based data exchange. The expanding volume of individual and sensitive information being digitally gathered by the AEC industry and store in the cloud, which makes it vulnerable to cyber attack. According to the Data Protection Act and the General Data Protection Regulation, organisation has to ensure security of people and security of sensitive data. Subsequently, it is crucial to establish the framework or method to provide the privacy of project data. Specifically, individual information, for example, individual health records, address and all other background information should be protected as per the legal regulation In this research paper, a technique called Hybrid-k anonymity has been proposed to protect the individual’s personal information as well as employees details, supplier details, cost details. In this technique, we modified original data using randomisation technique and then apply anonymisation on modified data which can provide better accuracy with minimum loss of information. This approach will enhance the privacy of sensitive information from cyber attack of the data miner.