CT-T: Collaborative Research: Preserving Utility While Ensuring Privacy for Linked Data

Johannes GehrkeCornell University小组：P060970CT：T Collaborative Research：保护效用，同时确保链接数据的隐私摘要本研究调查如何在发布数据的同时限制对数据中实体的披露。人口普查数据就是一个例子，它是社会经济数据的宝贵来源。限制披露的简单方法，如删除社会安全号码和姓名等识别属性，是不够的，因为数据中其他信息的组合可以帮助识别数据中的个人，特别是在数据可以链接到外部数据库的情况下。正是这种联系，以及通常与其他数据显式链接的数据属性，才是本项目的重点。在关联数据中，数据记录通过记录之间的关系进行链接。例如，关于学生和他们所上的课程的数据，其中链接是学生和她所上的课程之间的关联；关于网络分组和转发这些分组的路由器的数据，其中链接是分组与路由器的关联；或者关于人及其社交网络的数据，其中链接是人与人之间的社会关系。正是这些链接在数据中的显式表示违反了先前工作的一些关键假设。这项研究涵盖了整个领域，从激励关联数据的应用，到新的隐私模型和实用的匿名算法，再到攻击和分析匿名数据的新技术。

Johannes GehrkeCornell University0627680Panel: P060970CT: T Collaborative Research: Preserving Utility While Ensuring Privacy for Linked DataAbstractThis research investigates how to publish data while limiting disclosure about entities in the data. An example is census data, an invaluable source of socioeconomic data. Simple approaches for limiting disclosure, such as removing identifying attributes like social security number and name, are not sufficient because combinations of other information in the data can help identify individuals in the data, especially when the data can be linked to external databases. It is this linkage, and in general, the property of data that it is often explicitly linked to other data, that is the focus of this project. In linked data, data records are linked through relationships between records. Examples include data about students and the classes they took where the links are the association between a student and the classes she took; data about network packets and the routers that forwarded these packets, where the links are the association of packets to routers; or data about people and their social network, where the links are the social relationships between people. It is the explicit representation of these links in the data that violates some of the key assumptions of prior work. This research spans the whole spectrum from motivating applications of linked data, to novel privacy models and practical anonymization algorithms, to new techniques for attacking and analyzing anonymized data.