SGER: Blind Evaluation of Spatial Queries with Hilbert Curves to Preserve Location Privacy

SGER：使用希尔伯特曲线对空间查询进行盲评估以保护位置隐私

• 批准号: 0742811
• 负责人: Cyrus Shahabi
• 金额: --
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-09-01 至 2009-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0742811&HistoricalAwards=false
• 关键词: SGER; Blind; Evaluation; Spatial; Queries

This project develops a fundamentally new model for preserving location privacy in spatial query evaluation. The main novelty is that unlike the current approaches for location privacy, this model does not require an intermediate party between the client and server during the regular mode of communication. This feature is consistent with the typical encryption models where two parties, Alice and Bob, can communicate privately without sending their messages through a third party intermediator. This is a high-risk research because devising an encryption scheme that maintains distance properties of space is hard. That is, with conventional encryption approaches, nearby objects in the original space are no longer close in the encrypted space. The idea is to devise a one-way transformation on the space such that the transformed space maintains the distance properties of the original space and hence spatial queries can be resolved efficiently in the transformed space. This transformation can be viewed as encryption of the space with a one-way transformation function that allows fast computation of its inverse given some extra knowledge, termed transformation-key.The broader impact of this study is to enable many users with privacy concerns to utilize customized location services in their mobile devices without compromising their location privacy. This in turn would increase the customer base for many industries and government agencies in the areas of geospatial information systems, online maps, car navigation systems, Location-Based Services, and military/intelligence operations. One PhD student is supported by this project. The encryption tools and the technical papers describing them will be disseminated through the project's web site: http://infolab.usc.edu/projects/LocationPrivacy/.

该项目开发了一种从根本上保存空间查询评估中的位置隐私的新模型。 主要的新颖性是，与当前的位置隐私方法不同，该模型在常规通信模式下不需要客户和服务器之间的中间聚会。 此功能与典型的加密模型一致，在该模型中，爱丽丝和鲍勃都可以私下交流而无需通过第三方中间人发送消息。这是一项高风险研究，因为设计一种维持空间距离特性的加密方案很难。 也就是说，使用常规的加密方法，原始空间中的附近对象不再在加密空间中关闭。 这个想法是在空间上设计一个单向转换，以便转换的空间保持原始空间的距离属性，因此可以在变换的空间中有效地解决空间查询。可以将这种转换视为具有单向转换功能的空间的加密，该功能可以在给定一些额外的知识（称为转换键）的情况下快速计算其反向。这项研究的更广泛影响是使许多具有隐私问题的用户能够在其移动设备中使用自定义的位置服务而无需损害其位置私密。反过来，这将增加地理空间信息系统，在线地图，汽车导航系统，基于位置的服务以及军事/情报运营领域的许多行业和政府机构的客户群。该项目为一名博士学位学生提供了支持。 加密工具和描述它们的技术论文将通过项目的网站进行传播：http：//infolab.usc.edu/projects/locationprivacy/。