关于几何和地理综合的可逼近性及Min-Max面元覆盖问题
On the Approximability of Geometric and Geographic Generalization and the Min-Max Bin Covering Problem
摘要：
我们研究的问题，抽象的个人数据表，使没有选择查询可以识别少于k个人。我们表明，这是不可能实现任意好的多项式时间近似的一些自然变化的推广技术，除非P = NP，即使当表只有一个单一的准识别属性，代表一个地理或无序属性：邮政编码：节点的平面图推广到连接的子图GPS坐标：点在R2推广到非重叠矩形无序数据：可以任意分组的文本标签。除了不可能的结果，我们提供了近似算法这些困难的单属性泛化问题，其中，当然，适用于多属性的情况下，一个是准识别。我们从理论和实验上表明，我们的近似算法可以合理地接近最优解。顺便说一句，无序数据的泛化问题可以被看作是一种新类型的装箱问题-最小最大箱覆盖-这可能是独立的利益。
英文摘要：
We study the problem of abstracting a table of data about individuals so that no selection query can identify fewer than k individuals. We show that it is impossible to achieve arbitrarily good polynomial-time approximations for a number of natural variations of the generalization technique, unless P = NP, even when the table has only a single quasi-identifying attribute that represents a geographic or unordered attribute: Zip-codes: nodes of a planar graph generalized into connected subgraphs GPS coordinates: points in R2 generalized into non-overlapping rectangles Unordered data: text labels that can be grouped arbitrarily. In addition to impossibility results, we provide approximation algorithms for these difficult single-attribute generalization problems, which, of course, apply to multiple-attribute instances with one that is quasi-identifying. We show theoretically and experimentally that our approximation algorithms can come reasonably close to optimal solutions. Incidentally, the generalization problem for unordered data can be viewed as a novel type of bin packing problem--min-max bin covering--which may be of independent interest.
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