欢迎您！东篱公司
退出
-
申报数据库
-
立项数据库
-
成果数据库
-
项目获奖数据库
中文摘要：
由于位置感知移动电子设备的繁荣,位置服务（LBS）几乎在所有的社会和商业领域广泛流行.虽然LBS给个人和社会带来了巨大利益,但也给用户的隐私造成了严重威胁.因为用户享受LBS的同时需要向不可信的LBS提供商泄露其位置和查询属性,而附加在这些信息上的上下文揭露了用户的兴趣爱好、生活习惯、健康状况等.如何保护用户的隐私免受恶意提供商的侵犯,对LBS生态系统的健康发展至关重要,因而引起了研究者的广泛关注.对LBS隐私保护的研究现状与进展进行综述.首先介绍LBS隐私的概念和威胁模型;然后,从系统结构、度量指标、保护技术等方面对现有的研究工作进行细致的分类归纳和阐述,重点阐述当前LBS隐私保护研究的主流技术：基于扭曲法的隐私保护技术;通过对各类技术性能和优缺点的分析比较,指出了LBS隐私保护研究存在的问题及可能的解决方法;最后,对未来研究方向进行了展望.
英文摘要：
Location-based service （LBS） has recently become popular in almost all social and business fields due to the boom of location-aware mobile electronic devices. LBS, albeit providing enormous benefits to individuals and society, poses a serious threat to users＇ privacy as they are enticed to disclose their locations and query attributes to untrusted LBS providers via their LBS queries. Moreover, the contextual information attached to these locations and service attributes can reveal users＇ personal interests, life styles, health conditions, etc. How to preserve users＇ privacy against potentially malicious LBS providers is of vital importance to the well-being of LBS ecosystem, and as such, it attracts great attentions from many researchers. This paper provides a review of the state-of-the-art of privacy preserving for LBS. First, the concept and threat model of LBS privacy are presented. Then, the existing schemes for preserving users＇ LBS privacy are described in detail from the aspects of architecture, metric and technology. Next, a pointed discussion is placed on the latest mainstream technology, with emphasis on the distortion-based technology. Further, following a comprehensive comparison and analysis of the performance and defects of various technologies, the problems and possible solutions for LBS privacy preserving are pointed out. Finally, some future research directions are provided.
同期刊论文项目
同项目期刊论文
Dynamic Recommendation Trust Model Based on Information Entropy and Heuristic Rules in E-Commerce En
期刊信息
位置：