As data privacy becomes paramount, data anonymization emerges as a crucial technique for re-engineering datasets containing sensitive personal identifiable information (PII). This study conducts a comparative analysis of various anonymization measures, including K-anonymity, L-Diversity, and T-Closeness, as implemented in Basic Mondrian, Mondrian L-Diversity, Mondrian, DataFly, and Top-Down Greedy algorithms. These algorithms vary in terms of their computational complexity and data utility preservation, offering different levels of privacy protection. The research aims to evaluate the practical advantages and disadvantages of these techniques in real-world scenarios and identify the most suitable anonymization procedure for specific privacy requirements and data characteristics.