Privacy-Preserving Data Mining Techniques for Sensitive Data Analysis

Abstract

The rapid growth of data-driven technologies has raised concerns about the privacy and security of sensitive information. Privacy-preserving data mining (PPDM) has emerged as a critical field that seeks to balance the need for extracting valuable insights from data while safeguarding individuals' privacy. This study explores state-of-the-art techniques for privacy-preserving data mining, focusing on methods such as differential privacy, homomorphic encryption, secure multi-party computation, and data perturbation. We discuss the strengths and limitations of each approach, their applicability to various data types, and their impact on data utility and mining accuracy. Furthermore, the paper highlights recent advancements in PPDM, addressing challenges such as computational efficiency, scalability, and compliance with privacy regulations. The growing demand for privacy-aware analytics underscores the importance of developing robust and efficient PPDM techniques, making them essential for industries handling sensitive information, such as healthcare, finance, and social media.