Privacy-preserving Association Rule Mining: Techniques and Applications
DOI:
https://doi.org/10.53032/tvcr/2025.v7n2.38Keywords:
Association rule mining, Cryptographic methods, Data anonymization, Differential privacy, Privacy protection, Privacy-preserving algorithms, Secure multi-party computation (SMC)Abstract
The goal of privacy-preserving association rule mining (PPARM) is to identify useful patterns in datasets while protecting sensitive data. Protecting privacy is essential in the big data era, particularly when handling private, sensitive, or financial information. Sensitive information may be exposed since traditional ARM algorithms like Apriori and FP-growth do not handle privacy concerns. This paper explores privacy-preserving techniques such as data anonymization, cryptographic methods, secure multi-party computation (SMC), and differential privacy. It examines the trade-offs between maintaining data utility and privacy, addressing challenges like scalability and efficiency. Real-world applications in healthcare, e-commerce, and finance are discussed, highlighting the importance of privacy. Emerging trends aim to develop advanced models that balance privacy with effective data analysis.
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