Privacy-Preserving Techniques in Data Mining: A Comprehensive Analysis of Homomorphic Encryption and Differential Privacy Approaches

Abstract

The proliferation of big data analytics has raised significant privacy concerns regarding the protection of sensitive information during data mining processes. This research investigates the effectiveness of homomorphic encryption (HE) and differential privacy (DP) as privacy-preserving techniques in data mining applications. Through systematic analysis of existing implementations, this study evaluates the performance, security guarantees, and practical applicability of these approaches across various data mining tasks including classification, clustering, and association rule mining. Our findings reveal that while fully homomorphic encryption offers comprehensive security guarantees, it suffers from prohibitive computational overhead for large-scale data mining applications. In contrast, somewhat homomorphic encryption schemes provide a more practical balance between security and efficiency. Differential privacy demonstrates superior performance in terms of computational efficiency, though with varying utility-privacy tradeoffs dependent on privacy budget allocation. We propose a hybrid framework that leverages the strengths of both approaches, demonstrating improved privacy protection without significant utility loss on benchmark datasets. This research contributes to advancing privacy-preserving data mining techniques that balance analytical utility with robust privacy guarantees.