Privacy-Preserving Synthetic Data Generation: A Comprehensive Survey

Abstract

Synthetic data generation has emerged as a powerful tool for addressing privacy concerns while enabling data-driven research and development. This comprehensive survey examines the current state of privacy-preserving synthetic data generation techniques, with particular focus on differential privacy, k-anonymity, and utility preservation methods.

Introduction

The increasing demand for data-driven insights across various domains has created a tension between the need for data access and the imperative to protect individual privacy. Synthetic data generation offers a promising solution by creating artificial datasets that preserve the statistical properties of original data while ensuring privacy protection.

Methodology

Our survey encompasses three main areas:

• Differential Privacy: Mathematical framework for quantifying privacy guarantees
• K-Anonymity: Traditional anonymization techniques and their modern variants
• Utility Preservation: Methods for maintaining data quality and statistical fidelity

Key Findings

The research reveals several important insights:

• Differential privacy provides strong theoretical guarantees but often requires careful parameter tuning
• K-anonymity remains relevant for many practical applications despite its limitations
• Utility preservation techniques are crucial for ensuring synthetic data remains useful for downstream tasks

Conclusion

Privacy-preserving synthetic data generation represents a critical area of research with significant practical implications. Future work should focus on developing more efficient algorithms and better understanding the trade-offs between privacy and utility.