Census Associated Multiple Attributes Data Release Based on Differential Privacy
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 ad-dress the consistency constraint among geographic regions of census statistics,but associated multi-attribute data with more com-plex 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 re-lease 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.
万方数据
维普
