Data generalization is a powerful technique for sanitizing multi-attribute data for publication. In a multidimensional model, a subset of attributes called the quasi-identifiers (QI) are used to define the space and a generalization scheme corresponds to a partitioning of the data space. The process of sanitization can be modeled as a constrained optimization problem where the information loss metric is to be minimized while ensuring that the privacy criteria are enforced. The privacy requirements translate into constraints on the partitions (bins), like minimum occupancy constraints for k-anonymity, value diversity constraint for l-diversity etc. Most algorithms proposed till date use some greedy search heuristic to search for a locally optimal generalization scheme. The performance of such algorithms degrade rapidly as the constraints are made more complex and numerous. To address this issue, in this paper we develop a complete enumeration based systematic search framework that searches for the globally optimal generalization scheme amongst all feasible candidates. We employ a novel enumeration technique that eliminates duplicates and develop effective pruning heuristics that cut down the solution space in order to make the search tractable. Our scheme is versatile enough to accommodate multiple constraints and information loss functions satisfying a set of generic properties (that are usually satisfied by most metrics proposed in literature). Additionally, our approach allows the user to specify various stopping criteria and can give a bound on the approximation factor achieved by any candidate solution. Finally, we carry out extensive experimentation whose results illustrate the power of our algorithm and its advantage over other competing approaches.
翻译:数据概括化是清洁多属性数据供出版使用的有力技术。 在多维模型中, 使用称为准识别符(QI)的一组属性来定义空间, 而一般化方案则相当于数据空间的分割。 清洁化过程可以模拟为一个有限的优化问题, 信息丢失指标将最小化,同时确保隐私标准得到执行。 隐私要求转化为分区( bins) 的制约, 如对k- 匿名性的最低占用限制、 多样性等的值多样性限制。 大多数到目前为止提议的算法使用一些贪婪的优势搜索过度, 以寻找本地最佳的通用化方案。 这些算法的性能随着限制的复杂和数量增多而迅速退化。 为了解决这一问题, 我们在本文件中开发了一个基于全面查点的系统搜索框架, 在所有可行的候选人中搜索全球最佳的普及计划。 我们采用了一种新型查点技术, 消除并发展有效的超额裁剪裁法, 从而缩小解决方案的空间。 大多数的算法都使用一些贪婪的搜索优势搜索超额搜索超额搜索方法。 我们的算法的算法, 最能让更多的用户最终确定一个量化标准。