Approaches to Preserve Privacy in Data Analytics for Health Care Sectors

Abstract

The privacy-preserving techniques in healthcare data analytics, addressing the challenge of balancing data insights with patient privacy. It covers traditional de- identification methods, advanced techniques like differential privacy, and explores emerging cryptographic approaches such as homomorphic encryption and secure multiparty computation. Real-world applications demonstrate the efficacy of these methods in preserving privacy during data processing. The paper also discusses challenges like computational overhead and interoperability, and suggests future research directions. Aimed at researchers, healthcare professionals, and policymakers, this comprehensive overview provides insights into current methodologies, responsible use of healthcare data for analytics.