基于差分隐私的人口普查关联多属性数据发布

Census Associated Multiple Attributes Data Release Based on Differential Privacy

发布未经保护的人口普查统计数据有泄露居民个人隐私信息的风险。基于差分隐私的人口普查数据保护方案已经得到研究者的广泛关注。在解决人口普查统计数据的地理区域之间的一致性约束时,具有更复杂层次性、一致性约束的关联多属性数据在现有方法下面临无法在单棵层次树中构建的挑战。文中提出了一种基于差分隐私的人口普查区域内部关联多属性统计数据最优一致发布方法,该方法能够实现复杂一致性约束统计数据的高效发布。首先将复杂的关联多属性之间的一致性约束划分为相对独立且易于求解的多重一致性约束,然后根据人口普查关联多属性数据的结构特性,通过数学分析在现有方法的基础上进行进一步的效率优化,最后结合多重一致性约束问题的逼近方法实现最优一致发布。在真实的人口普查数据集和合成数据集上进行实验,结果表明,所提方法能够在效率表现上优于同类方法1~2个数量级的同时保持与同类方法一致的精度。

The release of unprotected census statistics carries the risk of revealing residents’personal privacy information.Census data protection solutions based on differential privacy have received substantial attention from researchers.Existing methods address the consistency constraint among geographic regions of census statistics,but associated multi-attribute data with more complex hierarchical consistency constraints face the challenge of being unable to build in a single hierarchical tree under existing methods.In this paper,we propose a differentially privacy method for optimally consistent release of associated multiple attributes statistics within census regions,which can achieve efficient release of statistics with complex consistency constraints.Firstly,the consistency constraints among the complex associated multiple attributes are divided into relatively independent and easily solved multiple consistency constraints.Then,based on the structural characteristics of the census associated multiple attributes data,mathematical analysis is used to further optimize the efficiency based on the existing methods.Finally,the optimal consistent release is achieved by combining the approximation method of the multiple consistency constraints problem.Experiments on real census datasets and synthetic datasets show that the proposed method can outperform similar methods in efficiency performance by one to two orders of magnitude while maintaining the same accuracy as similar methods.