轨迹隐私保护研究综述

随着智能终端的普及和无线通信技术的发展,基于位置的服务已渗入到人们的日常生活当中.这些服务在给人们的日常生活带来便利的同时,也带来隐私泄漏的风险.针对轨迹数据的推理攻击不仅可分析出目标用户的家庭住址、工作地点等敏感位置信息,甚至可推测出用户的生活习惯、健康状态、宗教信仰等隐私信息.轨迹隐私能否得到妥善保护已成为制约移动互联网发展的瓶颈问题.本文对已有的轨迹隐私保护方法进行了分类描述,并分析已有工作的优缺点,最后指明未来的研究方向.     

一种主动扩散式的位置隐私保护方法

随着基于位置的信息服务的日益普及,位置信息的隐私保护已逐渐成为了一个突出的安全问题.因此,提出了一种基于主动共享机制的位置隐私保护方案,该方案引入位置服务信息的邻近共享机制,通过在邻居节点主动共享位置服务信息,有效降低了移动用户对位置服务器的依赖,从而提高了其位置信息的隐私性.论文分别采用病毒传播模型和仿真实验对方案进行理论分析和有效性验证.     

智能移动终端的位置隐私保护技术

从位置隐私保护理论模型和位置隐私保护方法两个方面入手,对智能移动终端上的位置隐私保护研究现状进行了探讨,重点研究当前模型和方法的优缺点以及各自的适用场景;结合智能终端和移动互联网的发展趋势,指出当前位置隐私保护技术存在的问题,对未来的研究方向提出了建议。     

基于预测和滑动窗口的轨迹差分隐私保护机制

为解决轨迹差分隐私保护中存在的隐私预算与服务质量等问题,提出了一种融合预测扰动的轨迹差分隐私保护机制。首先,利用马尔可夫链和指数扰动方法预测满足差分隐私和时空安全的扰动位置,并引入服务相似地图检测该位置的可用性;如果预测成功,则直接采用预测位置替代差分扰动的位置,以降低连续查询的隐私开销并提高服务质量。在此基础上,设计基于w滑动窗口的轨迹隐私预算分配机制,确保轨迹中任意连续的w次查询满足ε-差分隐私,解决连续查询的轨迹隐私问题。此外,基于敏感度地图设计一种隐私定制策略,通过自定义语义位置的隐私敏感度,实现隐私预算的量身定制,从而进一步提高其利用率。最后,利用真实数据集对所提方案进行实验分析,结果显示所提方案提供了更好的隐私保护水平和服务质量。     

基于差分隐私的连续位置隐私保护机制

针对连续使用基于位置的服务(LBS)会造成用户位置隐私泄露的问题,首先基于路网拓扑关系,提出了隐私级别划分算法——RPL算法,对敏感路段进行隐私级别划分.然后,提出差分隐私位置保护机制DPLPM,通过为敏感路段分配隐私预算并添加Laplace噪声,实现对位置数据的隐私保护.实验结果表明,所提机制能有效保护位置隐私,具有...     

位置服务中基于盲签名的隐私保护技术研究

针对传统的基于位置的服务(LBS)隐私保护模式的不足,提出了一种新的位置隐私保护技术,利用RSA密码体制,构造一种盲签名方案来实现用户真实身份的隐藏,从而达到了保护位置信息的目的,实现用户对位置隐私的完全控制,分析结果表明,该技术能有效降低通信开销以及服务器与客户端的处理代价。     

移动互联网下位置隐私保护及攻击技术研究

随着移动技术的日益发展,互联网从传统的PC端逐渐转向了以移动端为主要载体的新发展模式。位置服务在移动领域具有广泛的应用,但位置信息与用户身份的关联关系可能会被不法分子利用,给用户带来严重的伤害。文章研究了移动网络下的位置隐私保护,首先给出了三种位置服务结构,接着介绍了攻击者常用的攻击方法,并对典型的位置隐私保护技术进行了详细分析,最后结合服务需求和技术的发展趋势,指出了未来的保护技术研究重点。     

面向LBS的隐私保护模型及方案

首先,提出一种基于中心服务器结构的位置隐私保护模型,然后,针对该模型设计了一种基于伪随机置换的位置隐私保护方案,此方案借鉴k-匿名技术、秘密信息检索技术的设计理念和方法,实现了完美匿名和基于位置的盲查询。最后,证明此方案具备不可追踪性和不可关联性等安全属性,并对方案的效率问题进行了分析。     

情境感知的位置隐私保护方法研究进展

随着无线通信技术与智能移动终端的发展,基于位置的服务(LBS, location-based service)得到广泛应用,与移动对象位置相关的数据隐私保护已经成为LBS中的研究热点。首先简单介绍了位置隐私与情境感知的基本概念;其次,对现有的位置隐私保护方法从隐私保护效果、服务质量、系统结构和时空情境自适应性4个方面进行分析总结,指出了该研究的发展趋势;最后,对情境感知的位置隐私保护方法现状进行介绍,讨论了该领域存在的研究难点以及未来的研究方向。     

基于公交车缓存的车联网位置隐私保护方案

为了解决车联网中车辆用户使用基于位置的服务(LBS)时真实位置的泄露问题,提出一种基于公交车缓存的位置隐私保护方案.公交车先根据其线路信息向LBS提供商获取兴趣点(POI)池,然后在行驶时根据其当前位置从POI池中挑选部分POI数据形成POI列表并广播给周围私家车.私家车在接收到广播信息后,验证公交车身份,然后将POI...     

Dummy trajectory privacy protection scheme for trajectory publishing based on the spatiotemporal correlation

The spatiotemporal correlation was analyzed between neighboring locations and the trajectories similarity from the movement direction, the reachable time between neighboring locations and the movement distance, and a dummy trajectory privacy protection scheme based on the spatiotemporal correlation was proposed. Security analysis shows that the presented scheme successfully confuses the user's real trajectory with dummy trajectories, thereby pro-tecting the user's trajectory privacy. Furthermore, extensive experiments indicate that the presented scheme not only has the limited computation cost, but also ensures that the generated dummy trajectories are similar to the user's real trajectory.     

CP-ABE based users collaborative privacy protection scheme for continuous query

In location-based services (LBS),as the untrusted LBS server can be seen as an adversary,and it can utilize the attribute as background knowledge to correlate the real location of the user in the set of uncertain locations.Then the adversary can gain the location privacy when the user enjoys the snapshot and continuous query through the correlation inference attack.In order to cope with this attack,the main scheme in privacy protection is to generalize the attribute and achieve attribute anonymity.However,algorithms of this type usually assumes a trusted third party (TTP) which provides the service of similarity attribute finding and comparing,and it is unpractical in the real environment,as the TTP may become the point of attack or the bottleneck of service and it cannot be considered as the trusted one all the time.Thus,to cope with the correlation inference attack as well as the semi-trusted third party,ciphertext policy attribute based encryption (CP-ABE) and users collaboration based attribute anonymous scheme was proposed.In this scheme,the user coupled achieve location and attribute anonymity.Furthermore,this scheme could also provide security for attacks from the semi-trusted third party as well as semi-trusted collaborative users.At last,security analysis and the experiment results further verify the effectiveness of privacy protection and the efficiency of algorithm execution.     

车辆自组网的位置隐私保护技术研究

车辆自组网的位置服务在解决道路安全问题、为驾乘者提供便捷服务的同时,也带来了相应的隐私保护问题。总结了隐私保护内容,重点分析了车辆自组网的假名和签名2类隐私保护技术,其中假名方案分为基于特殊地形、基于安静时段、加密mix-zones和mix-zones通信代理;签名方案分为群签名和环签名。继而针对隐私保护水平的高低,分析了匿名集合、熵度量、数学理论分析和形式化证明几类主要的位置隐私度量方法,对其各自的特点进行了总结比较。     

LTPPM: a location and trajectory privacy protection mechanism in participatory sensing

The ubiquity of mobile devices has facilitated the prevalence of participatory sensing, whereby ordinary citizens use their private mobile devices to collect regional information and to share with participators. However, such applications may endanger the users' privacy by revealing their locations and trajectories information. Most of existing solutions, which hide a user's location information with a coarse region, are under k‐anonymity model. Yet, they may not be applicable in some participatory sensing applications that require precise location information. The goals are seemingly contradictory: to protect a user's location privacy while simultaneously providing precise location information for a high quality of service. In this paper, we propose a method to meet both goals. Through selecting a certain number of a user's partners, it can protect the user's location privacy while providing precise location information. The user's trajectory privacy can be protected by constructing several trajectories that are similar to the user's trajectory in an interval time T. Finally, we utilize a new metric, called slope ratio, to evaluate the partners' selection algorithm that we proposed. Then, we measure the privacy level that the location and trajectory privacy protection mechanism (LTPPM) can achieve. The analysis and simulation results show that LTPPM can protect the user's location and trajectory privacy effectively and also provide a high quality of service in participatory sensing. Copyright © 2012 John Wiley & Sons, Ltd.     

CLM:differential privacy protection method for trajectory publishing

In order to solve the problem existing in differential privacy preserving publishing methods that the independent noise was easy to be filtered out,a differential privacy publishing method for trajectory data (CLM),was proposed.A correlated Laplace mechanism was presented by CLM,which let Gauss noises pass through a specific filter to produce noise whose auto-correlation function was similar with original trajectory series.Then the correlated noise was added to the original track and the perturbed track was released.The experimental results show that the proposed method can achieve higher privacy protection and guarantee better data utility compared with existing differential privacy preserving publishing methods for trajectory data.     

Spatio-temporal aware privacy-preserving scheme in LBS

Location-based service (LBS) brings a lot of conveniences in people’s daily life,but the conveniences are accompanied with the leaking of privacy.A dummy-based location-preserving scheme was proposed,which took the correlation between spatial issues and temporal issues into account.Two algorithms were included in this scheme,map dividing algorithm and dummy contents determining algorithm.The map dividing algorithm divided the map into discrete location cells by Voronoi Diagram to ensure these discrete location cells were not adjacent to each other.The dummy contents determining algorithm replaced the query content in previous moment by the intending location in next moment,which efficiently avoided the adversary inferring mobile users’ sensitive information according to the correlation between spatial issues and temporal issues.The simulation experiments show that the proposed scheme is effective and efficient.     

基于图划分的个性化轨迹隐私保护方法

针对用户对轨迹匿名数据的个性化需求,提出一种基于轨迹间夹角和位置重合的(s, )-覆盖个性化轨迹间关联构建方法,并根据轨迹间距离和方向度量轨迹间边权,以构造规模可变的个性化轨迹图模型。同时,将轨迹k-匿名集的构建转化为轨迹图划分问题,提出了一种基于贪心策略寻找近似最优的k条轨迹构建轨迹k-匿名集的方法。通过对比实验,在合成轨迹数据集上验证了所提算法的有效性和合理性。     

Location privacy preserving scheme against attack from friends in SNS

In order to enrich the performance of the user's location information and to meet the diverse needs of users,a location privacy protection scheme based on attribute encryption was designed,which provided precise,more accurate,fuzzy and private four modes to manage the location information.The scheme was based on the algorithm of WT-CP-ABE.The location information was divided into three parts according to a close friend of grade,then the key infor-mation and position information was encrypted with attribute-based encryption and symmetric encryption method respec-tively and the ciphertext was published to the social network.The security of the scheme is analyzed,which shows that the scheme has the advantage of user attribute information confidentiality,data confidentiality and can resist the collusion attack.