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

CT-T：协作研究：在确保链接数据隐私的同时保留实用性

基本信息

批准号：
0627585
负责人：
Dan Suciu
金额：
$ 26.62万
依托单位：
University of Washington
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2006
资助国家：
美国
起止时间：
2006-09-15 至 2011-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=0627585&HistoricalAwards=false
关键词：
CT Collaborative Research Preserving Utility

项目摘要

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.

Johannes Gehrke Cornell大学0627680小组：P060970 CT：T合作研究：在确保关联数据隐私的同时保持实用性摘要这项研究调查了如何发布数据，同时限制对数据中实体的披露。一个例子是人口普查数据，这是社会经济数据的宝贵来源。限制披露的简单方法，例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，特别是当数据可以链接到外部数据库时。正是这种联系，以及通常数据的属性，即它通常与其他数据显式链接，是本项目的重点。在链接数据中，数据记录通过记录之间的关系链接。例子包括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;关于网络数据包和转发这些数据包的路由器的数据，其中链接是数据包到路由器的关联;或者关于人和他们的社交网络的数据，其中链接是人之间的社交关系。正是这些链接在数据中的明确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范围，从链接数据的激励应用，到新颖的隐私模型和实用的匿名化算法，再到攻击和分析匿名数据的新技术。