Privacy-Preserving Data Mining Techniques: A Mini Review

Abstract

Privacy-Preserving Data Mining (PPDM) has emerged as a crucial field
addressing the conflicting goals of maximizing data utility while protecting individual
privacy. This paper provides a comprehensive review of key PPDM techniques and models,
examining their strengths, weaknesses, and applications across various domains. We
analyze foundational privacy models such as Differential Privacy and K-anonymity, and
categorize major PPDM techniques including Data Perturbation, Data Anonymization,
and Cryptographic Methods. The paper evaluates these methods across different sectors
including healthcare, finance, and social media, highlighting domain-specific trade-offs
between privacy protection and data utility. We also explore emerging trends in PPDM,
particularly focusing on privacy-preserving machine learning models and tools for big
data environments. Finally, we identify significant challenges, such as scalability issues
with high-dimensional data and evolving privacy threats, that require continued research
attention to advance the field of privacy-preserving data mining.