Evaluating the Utility-Privacy Trade-off in Healthcare Data Synthesis

Abstract

Healthcare data synthesis presents unique challenges due to the sensitive nature of medical information and the critical importance of data accuracy. This paper examines the trade-off between data utility and privacy preservation in healthcare data synthesis, providing practical recommendations for implementation.

Introduction

Healthcare organizations face increasing pressure to share data for research purposes while maintaining strict privacy standards. Synthetic data generation offers a potential solution, but the unique characteristics of healthcare data require specialized approaches.

Methodology

We conducted a comprehensive evaluation using real healthcare datasets to assess:

• Privacy preservation effectiveness
• Statistical fidelity maintenance
• Clinical relevance preservation
• Implementation feasibility

Results

Our findings demonstrate that careful parameter selection can achieve both strong privacy guarantees and high utility preservation. Key factors include:

• Appropriate noise levels for differential privacy
• Domain-specific utility metrics
• Clinical validation requirements

Conclusion

Healthcare data synthesis is feasible with proper implementation, but requires domain expertise and careful consideration of both privacy and utility requirements.