Beyond federated learning: On confidentiality-critical machine learning applications in industry

Zellinger, Werner; Wieser, Volkmar; Kumar, Mohit; Brunner, David James; Shepeleva, Natalia; Gálvez, Rafa; Langer, Josef; Fischer, Lukas; Moser, Bernhard

doi:10.1016/j.procs.2021.01.296

Cited by 17 publications

(7 citation statements)

References 26 publications

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“…Only three papers [38,39,51] study the trade-of between data quality and security. Zellinger et al [51] address conidentiality protection in transfer learning (an ML approach focusing on storing knowledge gained while solving one problem and applying it to a diferent but related problem).…”

Section: Rq13: What Is the Trade-of Between Data Quality And Data Sec...mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.

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Section: Rq13: What Is the Trade-of Between Data Quality And Data Sec...mentioning

confidence: 99%

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

et al. 2023

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“…A large obstacle regarding data analysis in smart factories is data ownership, governance and security regarding distributed data sources [99]. This is especially evident when not only machine, but human data are also collected for data analysis.…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

“…This is especially evident when not only machine, but human data are also collected for data analysis. For this reason, Zellinger et al proposed a confidentiality-preserving transfer learning method to overcome this issue [99]. Another common problem in condition-based maintenance is concept drift when distribution of fault patterns changes over time.…”

Section: Data-driven Decision Makingmentioning

confidence: 99%

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

Lepasepp

Hurst

2021

Future Internet

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Ever since the emergence of Industry 4.0 as the synonymous term for the fourth industrial revolution, its applications have been widely discussed and used in many business scenarios. This concept is derived from the advantages of internet and technology, and it describes the efficient synchronicity of humans and computers in smart factories. By leveraging big data analysis, machine learning and robotics, the end-to-end supply chain is optimized in many ways. However, these implementations are more challenging in heavily regulated fields, such as medical device manufacturing, as incorporating new technologies into factories is restricted by the regulations in place. Moreover, the production of medical devices requires an elaborate quality analysis process to assure the best possible outcome to the patient. Therefore, this article reflects on the benefits (features) and limitations (obstacles), in addition to the various smart manufacturing trends that could be implemented within the medical device manufacturing field by conducting a systematic literature review of 104 articles sourced from four digital libraries. Out of the 7 main themes and 270 unique applied technologies, 317 features and 117 unique obstacles were identified. Furthermore, the main findings include an overview of ways in which manufacturing could be improved and optimized within a regulated setting, such as medical device manufacturing.

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“…One solution to this privacy and security problem is the use of privacy-preserving machine learning approaches [2], such as homomorphic encryption, federated learning, differential privacy, and secure multi-party computation. However, these new approaches are unfamiliar to most production workers faced with the challenges of using new data-based services.…”

Section: Introductionmentioning

confidence: 99%

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

Sielaff,

Hetfleisch,

Rader

2023

icat

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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.

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Beyond federated learning: On confidentiality-critical machine learning applications in industry

Cited by 17 publications

References 26 publications

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

A Systematic Literature Review of Industry 4.0 Technologies within Medical Device Manufacturing

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

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