Journal of Data Mining and Knowledge Engineering ISSN: 3139-5341 (Online)

Abstract

Privacy-preserving data mining (PPDM) has become increasingly crucial in the era of big data, where vast amounts of sensitive information are collected and analyzed for various purposes. This paper presents a comparative analysis of popular privacy preserving data mining techniques, evaluating their strengths and weaknesses in preserving individual privacy while extracting meaningful patterns from data. The techniques under scrutiny include homomorphic encryption, differential privacy, secure multiparty computation, and federated learning. We provide a comprehensive overview of each technique, analyze their applicability in different scenarios, and present a set of comparative tables to aid researchers and practitioners in selecting the most suitable method for their specific use case.

Keywords- Privacy-preserving data mining, Comparative analysis, Homomorphic encryption, Differential privacy, Secure multiparty computation, Federated learning, Computational overhead, Privacy guarantees, Data utility, Scalability.