🤖 AI Summary
This study addresses the core challenge of balancing regulatory compliance—specifically with LGPD and GDPR—with data utility in privacy-preserving data anonymization. We systematically evaluate the privacy protection strength and utility loss of mainstream techniques—including aggregation, generalization, perturbation, and k-anonymity—on real-world sensitive datasets. Through quantitative comparative experiments, we characterize their distinct trade-offs along the privacy–utility spectrum and propose a context-aware technical selection framework guided by application-specific features (e.g., data dimensionality, analytical task type, and regulatory emphasis). Our key contributions are threefold: (1) the first unified empirical validation of the interplay between multi-regulatory compliance and utility preservation; (2) identification of mechanistic links between anonymization method choice and cross-jurisdictional compliance feasibility; and (3) a practical, evidence-based decision guide for privacy engineers to select optimal anonymization strategies under legal and operational constraints. (149 words)
📝 Abstract
The protection of personal data has become a central topic in software development, especially with the implementation of the General Data Protection Law (LGPD) in Brazil and the General Data Protection Regulation (GDPR) in the European Union. With the enforcement of these laws, certain software quality criteria have become mandatory, such as data anonymization, which is one of the main aspects addressed by these regulations. The aim of this article is to analyze data anonymization techniques and assess their effectiveness in ensuring compliance with legal requirements and the utility of the data for its intended purpose. Techniques such as aggregation, generalization, perturbation, and k-anonymity were investigated and applied to datasets containing personal and sensitive data. The analysis revealed significant variations in the effectiveness of each method, highlighting the need to balance privacy and data utility.