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.
