Blockchain Secured Dynamic Machine Learning Pipeline for Manufacturing

Abstract

ML-based applications already play an important role in factories in areas such as visual quality inspection, process optimization, and maintenance prediction and will become even more important in the future. For ML to be used in an industrial setting in a safe and effective way, the different steps needed to use ML must be put together in an ML pipeline. The development of ML pipelines is usually conducted by several and changing external stakeholders because they are very complex constructs, and confidence in their work is not always clear. Thus, end-to-end trust in the ML pipeline is not granted automatically. This is because the components and processes in ML pipelines are not transparent. This can also cause problems with certification in areas where safety is very important, such as the medical field, where procedures and their results must be recorded in detail. In addition, there are security challenges, such as attacks on the model and the ML pipeline, that are difficult to detect. This paper provides an overview of ML security challenges that can arise in production environments and presents a framework on how to address data security and transparency in ML pipelines. The framework is presented using visual quality inspection as an example. The presented framework provides: (a) a tamper-proof data history, which achieves accountability and supports quality audits; (b) an increase in trust by protocol for the used ML pipeline, by rating the experts and entities involved in the ML pipeline and certifying legitimacy for participation; and (c) certification of the pipeline infrastructure, the ML model, data collection, and labelling. After describing the details of the new approach, the mitigation of the previously described security attacks will be demonstrated, and a conclusion will be drawn.

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