Privacy-preserving Sharing of Industrial Maintenance Reports in Industry 4.0

Knowledge sharing has proven its worth in many domains as an enabler for optimized processes and improved organizational agility. It makes problem-solving and decision-making much faster for the stakeholders. Knowledge sharing has also proven its effectiveness in the industrial maintenance domain as a key vector for the improvement of maintenance processes, that are still dominated by traditional practices. However, sensitive data disclosure remains one of the major roadblocks that prevent this trend from gaining ground in the manufacturing industry. In this paper, we present a new approach to provide remedies for sensitive data disclosure problems during the knowledge sharing activity. Relying on the semantic web, rule-based, and natural language processing techniques, our approach helps to detect and identify the potentially sensitive data in maintenance reports before being shared with other actors.