<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>The article presents an approach to data anonymization with the use of generally available tools. The focus is put on the practical aspects of using open-source tools in conjunction with programming libraries provided by suppliers of industrial control systems. This universal approach shows the possibilities of using various operating systems as a platform for process data anonymization. An additional advantage of the described approach is the ease of integration with various types of advanced data analysis tools based both on the out-of-the-box approach (e.g., business intelligence tools) as well as customized solutions. The discussed case describes the anonymization of data for the needs of sensitive analysis by a wider group of recipients during the construction of a predictive model used to support decisions.</p>
</abstract>

The article presents an approach to data anonymization with the use of generally available tools. The focus is put on the practical aspects of using open-source tools in conjunction with programming libraries provided by suppliers of industrial control systems. This universal approach shows the possibilities of using various operating systems as a platform for process data anonymization. An additional advantage of the described approach is the ease of integration with various types of advanced data analysis tools based both on the out-of-the-box approach (e.g., business intelligence tools) as well as customized solutions. The discussed case describes the anonymization of data for the needs of sensitive analysis by a wider group of recipients during the construction of a predictive model used to support decisions.

Automated Anonymization of Sensitive Data on Production Unit

Journal of Automation, Mobile Robotics and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Automated Anonymization of Sensitive Data on Production Unit"}

The article presents an approach to data anonymization with the use of generally available tools. The focus is put on the practical aspects of using...

Automated Anonymization of Sensitive Data on Production Unit

Online veröffentlicht: 26. Dez. 2023

Seitenbereich: 40 - 44

Eingereicht: 11. Jan. 2022

Akzeptiert: 07. Sept. 2022

DOI: https://doi.org/10.14313/jamris/1-2023/5

Schlüsselwörter
Data anonymization, Sensitive data, opensource tools, Industrial data processing, Historian data anonymization, Honeywell DCS, IT/OT integration, Operational technology

© 2023 Marcin Kujawa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Automated Anonymization of Sensitive Data on Production Unit

Online veröffentlicht: 26. Dez. 2023

Seitenbereich: 40 - 44

Eingereicht: 11. Jan. 2022

Akzeptiert: 07. Sept. 2022

DOI: https://doi.org/10.14313/jamris/1-2023/5

SchlüsselwörterData anonymization, Sensitive data, opensource tools, Industrial data processing, Historian data anonymization, Honeywell DCS, IT/OT integration, Operational technology

© 2023 Marcin Kujawa et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
Data anonymization, Sensitive data, opensource tools, Industrial data processing, Historian data anonymization, Honeywell DCS, IT/OT integration, Operational technology