Unitanony: a fine-grained and practical anonymization framework for better data utility

Abstract In order to share data without revealing private information, privacy-preserving data publishing techniques are proposed. K-anonymity and l -diversity secure against identity and attribute disclosure. Anonymization algorithms enforce the above models and are willing to reach two primary goals: achieving the privacy objective while maximizing data utility. Even though anonymization has been studied for decades, finding efficient techniques to improve data utility is an open question. It is a crucial challenge that impacts many anonymized data on the web, cloud, and IoT environments. However, some factors incur huge information loss for existing works. The original dataset may be transformed into generalized data to an excessive extent. To address this problem, we give a new framework and propose a heuristic algorithm called UnitAnony. It builds a full-coverage hierarchy for more generalization candidates and proposes an interval-mapping technique for fine-grained generalization extent. However, these improvements raise another challenge. It is the vast cost because more generalization will derive many operations for forming new values, grouping records, and verifying anonymization models. Therefore, we designed a data structure unit to generalize records at low costs and implemented a skipping strategy to execute the algorithm within an acceptable time. Besides, our algorithm can support many models. By evaluating well-known K-anonymity and l -diversity on real-world datasets, i.e., Adult and Census datasets, the experimental results demonstrate that our algorithm outperforms the existing algorithms (e.g., Mondrian, Top-down, Improved-Clustering, Flash, and Incognito) regarding data utility and effectiveness.