Evaluating Privacy-Preserving Data Analysis Techniques for Encrypted Health Records

The urgency of safeguarding patient privacy while facilitating access to healthcare data has become an essential focus in the field of health informatics. As organizations transition to electronic health records (EHRs), the challenge of conducting meaningful data analysis on encrypted records emerges as a critical concern. This study evaluates various privacy-preserving data analysis techniques that enable healthcare providers to analyze sensitive health information while ensuring data confidentiality.A comprehensive framework is developed to analyze key techniques such as homomorphic encryption, secure multiparty computation, and differential privacy. This framework allows for the assessment of these methods against critical criteria, including computational efficiency, data utility, scalability, and compliance with healthcare regulations such as HIPAA. Quantitative metrics are utilized to evaluate the performance of these techniques within simulated healthcare datasets, while qualitative insights are gathered from healthcare data specialists to understand practical implications and challenges associated with their implementation.Preliminary findings indicate that while homomorphic encryption allows for computation on encrypted data without the need for decryption, it can suffer from performance drawbacks that limit its applicability in real-time environments. Secure multiparty computation demonstrates strong privacy guarantees but may introduce significant overhead in terms of computational resources and inter-party communication. Differential privacy offers a robust approach to anonymizing data, making it suitable for sharing and collaborative analytics; however, trade-offs concerning data accuracy must be carefully managed.This study offers valuable insights into the practical implications of deploying these privacy-preserving techniques in healthcare settings. By providing a thorough evaluation of their strengths and limitations, this research aims to guide healthcare organizations in the selection and implementation of effective data analysis methods that uphold patient privacy and protect sensitive health information.