Collaborative Data Anonymization Framework With Energy Industry
DOI:
https://doi.org/10.52825/ocp.v9i.3314Keywords:
Anonymization, Synthetic Data, Data Sharing, Energy Data ManagementAbstract
Data anonymization is essential for data privacy, enabling organizations, especially in the energy sector with IoT data, to comply with regulations while using sensitive information. Despite many existing anonymization methods and tools, it lacks clarity on integrating these tools into a complete process and ensuring effective collaboration with data providers. To overcome these gaps, this paper proposes a collaborative data anonymization framework that efficiently chains open-source tools to streamline the process and improve data provider involvement. The findings indicates while these tools can detect sensitive information and generate anonymized
data, there are still limitations in metadata detection.
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Copyright (c) 2026 Yuting Gao, Zhiyu Pan, Antonello Monti
This work is licensed under a Creative Commons Attribution 4.0 International License.
Funding data
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Deutsche Forschungsgemeinschaft
Grant numbers 501865131