Pflughoeft Kurt A, Soofi Ehsan S, Soyer Refik
School of Business and Economics, University of Wisconsin-Stevens Point, Stevens Point, WI 54481, USA.
Sheldon B. Lubar School of Business, University of Wisconsin-Milwaukee, Milwaukee, WI 53211, USA.
Entropy (Basel). 2022 May 10;24(5):670. doi: 10.3390/e24050670.
Preserving confidentiality of individuals in data disclosure is a prime concern for public and private organizations. The main challenge in the data disclosure problem is to release data such that misuse by intruders is avoided while providing useful information to legitimate users for analysis. We propose an information theoretic architecture for the data disclosure problem. The proposed framework consists of developing a maximum entropy (ME) model based on statistical information of the actual data, testing the adequacy of the ME model, producing disclosure data from the ME model and quantifying the discrepancy between the actual and the disclosure data. The architecture can be used both for univariate and multivariate data disclosure. We illustrate the implementation of our approach using financial data.
在数据披露中保护个人隐私是公共和私人组织首要关注的问题。数据披露问题的主要挑战在于发布数据时，既要避免入侵者滥用，又要向合法用户提供有用信息以供分析。我们针对数据披露问题提出了一种信息论架构。所提出的框架包括基于实际数据的统计信息开发最大熵（ME）模型、测试ME模型的充分性、从ME模型生成披露数据以及量化实际数据与披露数据之间的差异。该架构可用于单变量和多变量数据披露。我们使用金融数据说明了我们方法的实现。
