Evaluation Framework for the Use of Privacy Preserving Technologies for Production Data

Abstract

To prevent unplanned machine downtime in production, machine conditions can be monitored and even predicted using condition and failure models based on current machine and process data. As most of these models are data-intensive, machine users often do not have enough data to develop these models themselves and want to collaborate with other companies. Since these models often require critical and classified machine and process data, which could be extracted from the models using attacks such as model inversion, sharing existing models between companies is not an option as it leaves one party vulnerable. Privacy preserving technologies such as homomorphic encryption, differential privacy, federated learning and secure multi-party computation can help overcome this problem. With the help of these approaches, there is no need to transmit sensitive data unencrypted to third parties in order to cooperate and take advantage of high-performance models. The aim of this paper is to first summarize the current state of research on privacy-preserving technologies in production, and then to provide a simple to use evaluation method and criteria. The focus is on enabling production workers to make informed decisions and exploit the full potential of existing data without the need for prior knowledge of privacy-preserving technologies. Finally, the evaluation method is validated using two example use cases in a production environment and the results are discussed.