(r, k, ε)-Anonymization: Privacy-Preserving Data Publishing Algorithm Based on Multi-Dimensional Outlier Detection, k-Anonymity, and ε-Differential Privacy

In recent years, there has been a tremendous rise in both the volume and variety of big data, providing enormous potential benefits to businesses that seek to utilize consumer experiences for research or commercial purposes. The general data protection regulation (GDPR) implementation, on the other hand, has introduced extensive control over the use of individuals’ personal information and placed many limits. Data anonymization technologies have become an important solution for businesses trying to generate value from data while adhering to GDPR limitations. To address these challenges, researchers have developed various methods, including k-anonymity and $\varepsilon $ -differential privacy, offering solutions for both industry and academia. However, protecting individuals’ privacy against diverse attack attempts presents significant challenges for anonymization models that rely solely on a single technique, highlighting the need for more adaptable and hybrid approaches. In this study, a new hybrid anonymization algorithm called (r, k, $\varepsilon $ )-anonymization has been proposed, which combines k-anonymity and $\varepsilon $ -differential privacy models in a consistent framework and provides stronger privacy guarantees compared to existing privacy-preserving models. The proposed algorithm is capable of overcoming well-known shortcomings of the k-anonymity and $\varepsilon $ -differential privacy models, and it has been confirmed by extensive tests on real-world datasets. The proposed (r, k, $\varepsilon $ )-anonymization algorithm outperforms k-anonymity and $\varepsilon $ -differential privacy in terms of the average error rate measure, achieving data utility increases of 31.74% and 26.99%, respectively.