This paper introduces a new theoretical scheme for the solution of the frequent itemset hiding problem. We propose an algorithmic approach that consists of a novel constraint-based hiding model which encompasses hiding into one pass mining, along with a solution methodology that relies on Linear Programming. The induced patterns by the constraint-based mining algorithm are, in this way, utilized to build a minimal linear program whose solution dictates the construction of a database extension that delivers the sought-for hiding. This extension should be appended to the original database and released as a whole for mining, with that resulting extended database hiding the sensitive knowledge that we want to protect. Our proposed theory outdoes both in space complexity and accuracy, all the existing approaches which have been proposed so far in this domain and we proved that superiority with a series of experiments against other existing approaches. Our proposal sheds a new light on the exploration of new algorithmic techniques which can be handily applied to model hiding problems by providing solutions that computationally outperform all existing modeling approaches for hiding.

本文介绍了一种解决频繁项集隐藏问题的新理论方案。问题。我们提出了一种算法方法，该方法包括一种新颖的基于约束的隐藏模型，该模型包含隐藏到单次挖掘中，以及一种依赖于线性规划的解决方案方法。以这种方式，基于约束的挖掘算法的诱导模式被用来构建一个最小线性程序，其解决方案决定了构建提供所寻求隐藏的数据库扩展。这个扩展应该附加到原始数据库并作为一个整体发布以进行挖掘，由此产生的扩展数据库隐藏了我们想要保护的敏感知识。我们提出的理论在空间复杂性和准确性方面都优于 迄今为止在该领域提出的所有现有方法，我们通过一系列针对其他现有方法的实验证明了其优越性。我们的提议为探索新的算法技术提供了新的思路，通过提供在计算上优于所有现有隐藏建模方法的解决方案，这些技术可以轻松应用于模型隐藏问题。