PrivRS: Differentially private synthetic data generation via role similarity - Ye, Xinxin; Zhu, Youwen; Pan, Jie; Zhang, Miao; Deng, Hai (Computers & security, 2025)
Generating high-quality synthetic data subject to differential privacy (DP) requirements remains a fundamental challenge, particularly for high-dimensional datasets. Existing methods often struggle to strike a balance between privacy guarantees and data utility while maintaining computational efficiency. To address their deficiencies, we propose PrivRS, a novel DP-compliant synthetic data generation framework that leverages role similarity (RS) to enhance data fidelity and utility. PrivRS introduces a structured approach to marginal selection by constructing a probabilistic complete graph G that encodes attribute correlations, effectively capturing complex dependencies. It then applies RS-based filtering to the data to identify informative marginals that satisfy DP constraints, reducing the level of noise injection while preserving key statistical properties. Finally, these post-processed noisy marginals are used to synthesize high-utility, privacy-preserving data. Extensive experiments on diverse datasets demonstrate that PrivRS achieves significant improvements over the state-of-the-art methods. Specifically, PrivRS enhances statistical query accuracy and clustering performance by at least 22% for high-cardinality domains while reducing computational overhead by an average of 28%. These results demonstrate PrivRS as a scalable and practical solution for differentially private data synthesis, capable to achieve a good trade-off among privacy, utility, and efficiency, while also performing well in terms of fairness, outlier behavior, and diversity.