Network Models for Complementary Cell Suppression
本文提出基于线性网络优化的互补单元格抑制方法，用于保护表格数据中的个体隐私，相比现有方法在理论完备性、可理解性和计算效率上具有优势。
Abstract Complementary cell suppression is a method for protecting data pertaining to individual respondents from statistical disclosure when the data are presented in tabular form. Several mathematical methods for complementary suppression have been proposed in the statistical literature; some have been implemented in large-scale data processing environments by national statistical agencies. Each method has either theoretical or computational limitations. This article presents solutions to the complementary cell suppression problem based on linear optimization over a mathematical network. These methods are shown to be optimal for certain problems and to offer theoretical and practical advantages, including comprehensiveness, comprehensibleness, and computational efficiency.