Privacy-preserving model on data with conditional functional dependency
基于条件函数依赖的隐私保护模型

There exists privacy threat in the publishing data that contains CFDs. This paper showed that the correlations among attributes by CFDs could bring potential vulnerability to privacy. This paper formalized the CFD-based privacy attack, proposed the privacy model l-deduction and developed an algorithm that achieved l-deduction. Beyond a theoretical treatment of the problem, the experiments also demonstrate the effectiveness of the proposed technique.