Bolstering the competitiveness of the machine tool industry

MINERVA research project: Privacy-enhancing technologies protect sensitive machine data

Press release /

© Fraunhofer AISEC
MINERVA research project: Secure collaborative utilization of machine tool data using privacy-enhancing technologies
Advancing digitalization and networking are bringing about ever-increasing volumes of data in the machine tool industry. To exploit its potential for data-driven innovations, the data needs to be aggregated and evaluated across companies. In the “Secure collaborative utilization of machine tool data using privacy-enhancing technologies” (MINERVA) research project, the Fraunhofer Institute for Applied and Integrated Security AISEC (project coordinator) is working together with partners to develop technologies for a data infrastructure that uses privacy-enhancing technologies to ensure the security of sensitive machine data. The research group also includes the Institute for Machine Tools and Industrial Management (iwb) at the Technical University of Munich, Hufschmied Zerspanungssysteme GmbH and Siemens AG.
 
Advancing digitalization and networking in production as part of Industry 4.0 are also offering the machine tool industry opportunities to make progress in terms of innovation. For example, the operators of data infrastructures are able to offer collaboratively trained machine learning applications which can achieve results in two different ways. Firstly, users can bring them into play in predictive condition monitoring to improve the efficiency and effectiveness of their production systems. Secondly, machine manufacturers can deploy them to systematically identify potential for optimization in their products. Implementing these applications requires production-related data to be aggregated and evaluated across companies. Yet here there is the concern that making data available will lead to companies losing their intellectual property and suffering competitive disadvantages as a result. For this reason, hardly any companies have so far shared data, despite this being the prerequisite for data-driven innovations (see Bitkom in German).
 

Machine learning models for predictive condition monitoring

In the “Secure collaborative utilization of machine tool data using privacy-enhancing technologies” (MINERVA) research project, the partners are using privacy-enhancing technologies to ensure machine operators’ sovereignty over the machine data, thereby protecting their intellectual property. Using anonymized data, the partners train suitable machine learning models for the predictive condition monitoring of machine tools which then work without disclosing sensitive data.

“For the machine tool industry, we are demonstrating at an interdisciplinary level that by selecting and using suitable technologies to promote data protection, the opportunities associated with sharing data can significantly outweigh the risks, not least because we are incorporating an additional level of security,” explains Bartol Filipovic, MINERVA project manager and head of the Product Protection and Industrial Security department at Fraunhofer AISEC.

 

Protection of sensitive data

In relation to the protection of personal data, privacy-enhancing technologies have previously been used to enable data such as patient information to be evaluated anonymously (e.g., using differential privacy). The project partners are now transferring the principles of this approach to data from the machine tool industry in order to protect the intellectual property of those disclosing the data. “If machine learning algorithms are trained with cross-company data in the cloud, it could be possible to track what kind of workpiece was manufactured (product geometry), to what extent the machine tool was utilized, how much energy was used during manufacture and the production rate used. From a user’s perspective, this is all highly sensitive information, so, in MINERVA, we are developing solutions to protect this,” says Bartol Filipovic, describing the added value of the research approach. In addition to differential privacy, the other privacy-enhancing technologies used in MINERVA include trusted execution environments, which establish a protected, attested area in the cloud, and federated learning, in which — instead of the data itself — only the ML models already trained in the edge are migrated to the cloud.

MINERVA is funded by the German Federal Ministry of Education and Research (BMBF) as part of the “IoT security in smart home, production and sensitive infrastructures” announcement. The project volume amounts to EUR 2.47 million, of which 70% is provided by the BMBF. The project will run from May 2023 until April 2026.

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