密码学报（中英文）2026，Vol.13Issue(4)：672-688,17.DOI:10.13868/j.cnki.jcr.000872
基于轻量级安全多方计算的分布式隐私数据合成
Lightweight Secure Multi-Party Computation for Distributed Private Data Synthesis
摘要
Abstract
In the era of big data,cross-entity data sharing has become crucial for exploring data value,while high-value data distributed across multiple entities faces privacy leakage risks.Existing dis-tributed privacy-preserving data synthesis schemes achieve differential privacy through high-overhead secure multi-party computation(MPC)protocols,which can only handle low-dimensional datasets and suffer from excessive computational and communication overhead.To address this issue,this study proposes a lightweight MPC-based differentially private data synthesis framework that separates pri-vacy mechanisms from post-processing,enabling efficient data synthesis:secure computing parties add noise to the statistical information of raw dataset via MPC protocols,while the server generates synthetic datasets in plaintext based on the noised results,relieving the computational bottlenecks of current schemes.Building on this framework,the distributed differentially private data synthe-sis scheme DisABSyn is presented,with core innovations including:restructuring the computational pipeline of the prototype scheme by replacing high-overhead steps requiring raw dataset access with collaborative computations between secure computing parties and the server,thereby reducing high-overhead ciphertext vector multiplications;a marginal partitioning-based privacy budget allocation strategy for vertical scenarios to enhance data utility in such settings.Experiments demonstrate that DisABSyn achieves synthetic data utility comparable to the prototype scheme in both horizontal and vertical scenarios.Compared to existing distributed data synthesis schemes,DisABSyn maintains higher data utility while significantly reducing the communication overhead of MPC,achieving an average reduction of 88%in horizontal scenarios and 99%in vertical scenarios.关键词
差分隐私/数据合成/安全多方计算Key words
differential privacy/data synthesis/secure multi-party computing分类
信息技术与安全科学引用本文复制引用
贾靖宇,李鑫豪,刘哲理..基于轻量级安全多方计算的分布式隐私数据合成[J].密码学报（中英文）,2026,13(4):672-688,17.基金项目
国家自然科学基金重点项目(62032012)Key Program of National Natural Science Foundation of China(62032012) （62032012）
