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Optimizing data privacy: an RFD-based approach to anonymization strategy selection

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Abstract

This paper presents a novel Anonymization Strategy Selection Framework that combines relaxed functional dependencies (RFDs) and particle swarm optimization (PSO) to balance data privacy and utility. Our approach extracts RFDs from datasets, generates diverse anonymization strategies using domain generalization hierarchies, and employs PSO for strategy optimization. We introduce a fitness function that balances k-anonymity and information loss. The framework's innovation lies in using RFDs to capture fine-grained data dependencies, enabling more nuanced anonymization. Evaluation on widely used UCI machine learning repository datasets show our framework outperforms existing techniques, achieving higher k-anonymity levels with lower information loss. Our adaptive approach generates hybrid strategies combining elements from multiple RFDs, resulting in superior privacy-utility trade-offs. This research advances privacy-preserving data publishing by providing a flexible, effective tool for generating anonymized datasets that maintain high utility for downstream analysis.

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Data availability

No datasets were generated or analyzed during the current study.

Notes

  1. https://github.com/AlirezaSN/data-anonymization-framework.

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Author information

Authors and Affiliations

  1. Computer Engineering Group, Faculty of Engineering, Arak University, Arak, Iran

    Alireza Sadeghi-Nasab & Mohsen Rahmani

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  1. Alireza Sadeghi-Nasab
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  2. Mohsen Rahmani
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Contributions

Alireza Sadeghi-Nasab contributed to conceptualization, methodology, software, data curation, visualization, writing—original draft preparation. Mohsen Rahmani was involved in supervision, writing—reviewing and editing, validation.

Corresponding author

Correspondence to Alireza Sadeghi-Nasab.

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Appendices

Appendix A

DGHs for adult dataset

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DGH for age attribute

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DGH for working-class attribute

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DGH for relationship attribute

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DGH for gender attribute

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DGH for education-level attribute

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DGH for race attribute

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DGH for occupation attribute

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Sadeghi-Nasab, A., Rahmani, M. Optimizing data privacy: an RFD-based approach to anonymization strategy selection. J Supercomput 81, 134 (2025). https://doi.org/10.1007/s11227-024-06642-4

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