位置服务隐私保护

doi:10.6040/j.issn.1671-9352.0.2018.128

摘要/Abstract

摘要：

隐私的泄露问题不仅包含用户提交的位置和查询数据,更是包含了其中隐藏的用户身份、兴趣爱好、社会习惯、行为模式等。对位置服务隐私保护的技术进行综述,介绍了位置服务的应用场景、位置隐私的攻击方法。对现有的隐私保护体系结构和保护技术进行整理归纳。对未来的研究进行了展望,并提出一种基于缓存的时空扰动方法和LBS隐私保护度量假设方案。

关键词: 位置服务, 位置隐私, 查询隐私, 轨迹隐私, 隐私保护

Abstract:

The disclosure of privacy involves not only the users' locations and queries data, but also the users' identities, interests, social habits, behavior patterns and privacy hidden in these data. A review of privacy preserving for location-based services (LBS) is provided. The application scenarios of location services is introduced, the method of location privacy attack is given. The existing research on the privacy protection system structure and protection technology is summarized. Finally, future research is prospected, and a cache-based spatiotemporal disturbance method and LBS privacy protection metric hypothesis scheme are proposed.

Key words: location service, location privacy, query privacy, trajectory privacy, privacy preservation

中图分类号:

TP393

康海燕,朱万祥. 位置服务隐私保护[J]. 《山东大学学报(理学版)》, 2018, 53(11): 35-50.

Hai-yan KANG,Wan-xiang ZHU. Privacy preservation for location-based services[J]. JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE), 2018, 53(11): 35-50.

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表1

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表2

LBS隐私保护技术方法比较"

分类	技术	代表方法	体系结构	隐私保护度	服务质量
政策	IETF	GeoPriv^[11]		低；取决于监管力度，对不法攻击者约束有限	优
政策	W3C	P3P^[12]		低；取决于监管力度，对不法攻击者约束有限	优
匿名	匿名	Interval Cloak^[13]	集中式	高；取决于匿名度	差，由匿名度决定的隐匿面积衡量
		Casper Cloak^[14]	集中式	高；取决于匿名度	中等，相对Interval Cloak^[22]需要更小的匿名空间
		Clique Cloak^[15]	集中式	高；取决于匿名度和请求半径	良好，可满足个性服务需求和服务质量
		Hilbert Cloak^[16]	集中式	高；能够抵御位置推断攻击	良好
		YCWA^[17]	集中式	高；取决于匿名度	中等，等长轨迹匿名集信息损失大
		Gao^[18]	集中式	中等；仅考虑了轨迹角度在[π, π/2]时对轨迹相似性度量影响	良好，贪心算法保证质量与安全的平衡
		Wu^[19]	集中式	高；取决于轨迹形状因素	良好，受匿名度影响
		Wang^[20-21]	集中式	高；取决于匿名度	良好，贪心算法平衡安全和和服务
		Mask K^[22]	集中式	高；取决于一直发布的概率向量	良好
		Li^[23]	集中式	高；基于用户历史轨迹分析添加假轨迹	良好，运行效率和服务质量都有提高
		Lin^[24]	集中式	高；取决于轨迹相似性	良好，更高的假轨迹生成效率和服务率
	混合区	Mix-zone^[25-27]	集中式	中等；取决于混合区用户假名和混合区大小	良好，与混合区隐私保护度成反比
		Liu^[28-29]	集中式	高；取决于人口密度和交通互异性	良好，取决于多粒度混合区的划分
		Sun^[30]	集中式	高	良好
扰乱	假位置	PAD^[33]	独立式	低；取决于真实点与锚点的距离	优，可精确筛选出真实点结果
		SpaceTwist^[34]	分布式	低；取决于真实点与锚点的距离	优，精确查询
		Landmark^[35]	独立式	低；取决于真实点与地标的距离	中等，得到的是地标结果，受与真实位置距离影响
		Zhou^[36]	分布式	中等；针对语义进行了保护，取决于真实点与锚点的距离	中等，得到的是锚点结果，受与真实位置距离影响
		Niu^{[31, 37]}	分布式	高；取决于假位置的分布规律以及相似查询度	良好，受用户访问地点概率影响
		Hara^[38]	分布式	高；取决于真实环境约束	良好
		Do^[39]	分布式	高；取决于用户访问的概率	良好
	假查询	DUMMY-Q^[32]	分布式	中等；取决于假查询的真实性和多样性	高，精确查询
	差分隐私	Assam^[40]	集中式	中等；保护轨迹但仅考虑时间维度，不够全面	良好，有噪声干扰
		Chen^[41]	集中式	高；取决于公布的简单轨迹位置	良好，有噪声干扰
		PriLocation^[42]		高	优，显著降低位置频次稀疏性的噪声
		Wang^[43]	集中式	高	良好，有噪声干扰
		Diff_Anonmity^[44]	集中式	高，与隐私预算成反比	良好，与隐私预算成正比
		DPLRM^[45]	集中式	高	良好，受发布轨迹时长的限制，整体可用性受-隐私影响
		Bi^[46]	集中式	高；取决于匿名度	良好，受用户隐私级别设置影响
		GPOL^[47]	集中式	高	良好，取决于质心位置
加密	隐私信息检索	PIR^[48]	分布式	高；取决于PIR协议安全性	良好，仅能处理近邻查询，计算开销大
		cPIR^[50]	分布式	高；取决于PIR协议的安全性	优，计算开销较PIR^[45]有所降低
		Yi^[51]	集中式	高；依赖于同态加密(FHE)技术安全性	优，算法效率更高
		PRN_kNN^[52]	集中式	高；利用位随机数加密	优，预处理时间减少
		Fung^[53]	集中式	高；结合了PIR和差分隐私	优，查询效率提高
		Rao^[54]	集中式	高	优；降低了通信损耗
		PRC_KNN^[55]	集中式	高；用户可动态调整加密粒度	优，可个性化设置
	Hilbert曲线加密	HilCloak^[56]	分布式	高；取决于加密函数的安全性	优
		Tian^[57]	分布式	高；自适应的函数转换方法	优
		Hyeong^[58]	分布式	高	优；比现有加密方法查询效率更高
		Liang^[59]	分布式	高；能抵御推理攻击	优

表2

图10

图11

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结构类型	优点	缺点
独立式	技术简单,易于实现	仅对自身保护,缺乏全局信息,保护性弱,客户终端负担大
集中式	服务质量高,隐私保护效果好,移动客户端负担小	服务器易成为攻击目标,可信任服务器会成为系统瓶颈
分布式	摒弃第三方服务器,消除性能瓶颈,不存在第三方的泄露危险	增加了移动终端的通信和计算处理开销,算法效率相对较低,无法控制相对用户数量
混合式	安全性高,能够实现负载平衡	机构复杂,算法繁琐,实现困难

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