分析非结构化数据
Anonymizing Unstructured Data
摘要：
在本文中，我们考虑的问题，匿名化数据集，其中每个人都与一组项目，构成个人的私人信息。说明性数据集包括购物篮数据集和搜索引擎查询日志。我们形式化的概念的k-匿名的集值数据作为一个变种的k-匿名模型的传统关系数据集。我们定义了一个优化问题，从这个定义的匿名性，并提供了O（klogk）和O（1）-近似算法相同。我们证明了我们的算法的适用性，美国在线查询日志数据集。
英文摘要：
In this paper we consider the problem of anonymizing datasets in which each individual is associated with a set of items that constitute private information about the individual. Illustrative datasets include market-basket datasets and search engine query logs. We formalize the notion of k-anonymity for set-valued data as a variant of the k-anonymity model for traditional relational datasets. We define an optimization problem that arises from this definition of anonymity and provide O(klogk) and O(1)-approximation algorithms for the same. We demonstrate applicability of our algorithms to the America Online query log dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
