Automated anonymization of sensitive data on production unit

• Autorzy: Kujawa Marcin , Piotrowski Robert

Identyfikatory

DOI

10.14313/JAMRIS/1‐2023/5

• Języki publikacji: EN

Abstrakty

EN

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 ap‐ proach 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 anonymiza‐ tion 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.

Słowa kluczowe

EN

data anonymization; sensitive data; open source tools; industrial data processing; historian data anonymization; Honeywell DCS; IT/OT integration; operational technology

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2023
• Tom: Vol. 17, No. 1
• Strony: 40--44
• Opis fizyczny: Bibliogr. 9 poz., rys.

Twórcy

autor

Kujawa Marcin

• Gdańsk University of Technology, Faculty of Electrical and Control Engineering, Poland

autor

Piotrowski Robert

• Gdańsk University of Technology, Faculty of Electrical and Control Engineering, Poland

Bibliografia

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• [2] Nabywaniec, D. Anonymisation and masking of sensitive data in companies. ISBN: 978‐83‐283‐5681‐8 (in Polish), 2019.
• [3] Devaux, E. How “anonymous” is anonymized data? https://medium.com/statice/how‐anonymous‐is‐anonymous‐c92ad265a3e3 (2021‐11‐20).
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• [5] Grus J. Data Science from Scratch: First Principles with Python, ISBN: 978‐83‐283‐4603‐1, 2018.
• [6] Murthy, S., A. Abu Bakar, F. Abdul Rahim, and R. Ramli. A Comparative Study of Data Anonymization Techniques. 2019 IEEE 5th International Conference on Big Data Security on Cloud (Big‐DataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2019, pp. 306–309, doi: 10.1109/BigDataSecurity‐HPSC‐IDS.2019.00063.
• [7] Samad, A.A.; Arshad, M.M.; Siraj, M.M. Towards Enhancement of Privacy-Preserving Data Mining Model for Predicting Students’ Learning Outcomes Performance. 2021 IEEE International Conference on Computing (ICOCO), 2021, pp. 13–18, doi: 10.1109/ICOCO53166.2021.9673544.
• [8] Kenedy, P. What is werkzeug?. https://testdriven.io/blog/what‐is‐werkzeug/ (2021‐11‐20).
• [9] Grinberg, M. Flask Web Development: Developing Web Applications with Python, 2nd edition, ISBN: 978‐83‐283‐6384‐7, 2020.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).