L2P2: A location-label based approach for privacy preserving in LBS

The developments in positioning and mobile communication technology have made the location-based service (LBS) applications more and more popular. For privacy reasons and due to lack of trust in the LBS providers, k-anonymity and l-diversity techniques have been widely used to preserve privacy of users in distributed LBS architectures in Internet of Things (IoT). However, in reality, there are scenarios where the locations of users are identical or similar/near each other in IoT. In such scenarios the k locations selected by k-anonymity technique are the same and location privacy can be easily compromised or leaked. To address the issue of privacy preservation, in this paper, we introduce the location labels to distinguish locations of mobile users to sensitive and ordinary locations. We design a location-label based (LLB) algorithm for protecting location privacy of users while minimizing the response time for LBS requests. We also evaluate the performance and validate the correctness of the proposed algorithm through extensive simulations.     

Anonymous Query Processing in Road Networks

The increasing availability of location-aware mobile devices has given rise to a flurry of location-based services (LBSs). Due to the nature of spatial queries, an LBS needs the user position in order to process her requests. On the other hand, revealing exact user locations to a (potentially untrusted) LBS may pinpoint their identities and breach their privacy. To address this issue, spatial anonymity techniques obfuscate user locations, forwarding to the LBS a sufficiently large region instead. Existing methods explicitly target processing in the euclidean space and do not apply when proximity to the users is defined according to network distance (e.g., driving time through the roads of a city). In this paper, we propose a framework for anonymous query processing in road networks. We design location obfuscation techniques that: 1) provide anonymous LBS access to the users and 2) allow efficient query processing at the LBS side. Our techniques exploit existing network database infrastructure, requiring no specialized storage schemes or functionalities. We experimentally compare alternative designs in real road networks and demonstrate the effectiveness of our techniques.     

Changing information retrieval behaviours: an empirical investigation of users’ cognitive processes in the choice of location-based services

The introduction of smartphones and the accompanying profusion of mobile data services have had a profound effect on individuals’ lives. One of the most influential service categories is location-based services (LBS). Based on insights from behavioural decision-making, a conceptual framework is developed to analyse individuals’ decisions to use LBS, focusing on the cognitive processes involved in the decision-making. Our research is based on two studies. First, we investigate the use of LBS through semi-structured interviews of smartphone users. Second, we explore daily LBS use through a study based on diaries. The findings highlight that the decision to use LBS can be described by either a comparative mode based on the value of LBS in relation to other available options, or an intuitive mode in which past experiences trigger the use of heuristics. These modes in turn have positive influences on the continuance of LBS use and indicate changes in individuals’ information retrieval behaviours in everyday life. In particular, the distinct value dimension of LBS in specific contexts of use changes individuals’ behaviours towards accessing location-related information.     

LBS中基于移动终端的连续查询用户轨迹隐匿方法*

为减少现有LBS(基于位置的服务)机制给用户位置信息和个人隐私泄露带来的威胁,提出并实现了一个基于移动智能终端的连续查询用户运动轨迹保护方案.该方法利用移动终端来规划虚拟路径,以减少用户在连续查询中的隐私泄露,且不需要第三方服务器提供位置匿名服务,由用户自主决定何时启动位置隐匿机制.实验证明,提出的方法有效地隐匿了连续查询用户的位置及轨迹信息.     

基于社交网络好友攻击的位置隐私保护模型

随着无线网络的发展,移动社交网络用户发布其所在的地理位置信息时,如果包含敏感地理位置会导致用户隐私受到攻击。现有的位置隐私保护方法都是对用户发布的位置进行泛化处理,以牺牲用户的服务质量为代价,且大部分都是将攻击者定位在LBS服务商,没有考虑到统一对社交网络中的好友根据其可靠程度的不同提供不同准确度的地理位置信息。针对此问题,提出了基于社交网络好友亲密度分级的隐私保护模型L-intimacy,用来防止好友攻击者的攻击。理论分析和实验结果表明,与加入到Latitude服务的Google Maps相比,该方法既能保护移动社交网络用户的相关隐私,同时又具有较小的信息损失度。     

LBS-p:一个支持在线地图服务的LBS支撑平台

随着第三代通信技术(3G)和基于位置的服务(LBS)的发展,如何满足LBS用户随时随地的需要、以在线模式提供地图服务等GIS服务,成为一个重要的问题。针对LBS对于在线地图服务的需要,提出了一个基于轻量二进制矢量地图数据格式Byte-Map支持在线地图服务的LBS支撑平台LBS-p。LBS-p由运行在移动设备上的LBS-p mobile和服务器端的LBS-pserver组成。LBS-p mobile是一个基于Java ME平台的应用程序,负责在线地图数据的请求、管理及显示,以及LBS的访问与结果展示。LBS-pserver主要包括3部分:负责为在线地图服务准备地图数据的Byte-Map数据预处理机制;负责响应移动终端的在线数据请求,实时为其提供在线地图数据的Byte-Map数据服务模块;及面向LBS的GIS服务模块。LBS-p利用轻量地图数据格式Byte-Map,通过服务器端的在线地图服务模块和移动终端模块实现在线地图服务。     

基于本地差分隐私的众包隐私保护方法

在移动互联网发展的今天,基于位置服务（LBS）技术在移动互联上取得显著进展。针对个人用户进行精确定位时,数据信息隐私存在着泄露风险的问题,本文提出一种基于本地化差分隐私的地理不可区分性的扰动方法。在用户的真实位置数据信息流出客户端前采用地理不可区分性位置扰动方式,作用于真实位置以得到近似位置数据,服务器端收到后制成二级区域网格图,之后采用差分隐私对该图的工人计数进行扰动,最后在空间范围查询下进行实验验证,并与满足ε-本地化差分隐私扰动算法进行对比,精确度提高2.7%,同时与平均划分隐私预算分配方式进行实验对比,提高区域计数精确度4.57%。     

Recommendation system using location-based ontology on wireless internet: An example of collective intelligence by using ‘mashup’ applications

Location-Based Service (LBS) is considered as a key component of upcoming ubiquitous environments. A recommendation system based on LBS is expected to be an important service in ubiquitous environments, and most hardware technologies such as location estimation of a user by using Global Positioning System (GPS), as well as hi-speed internet access through cell phones, are currently supported. However, in terms of software, most services are driven and supported by a LBS service provider only. Consequently, lack of participation of users may occur in mobile environments. In this study, we suggest a LBS knowledge base inference platform with ontology which considers the current location and available time of users. Our knowledge base supports user participation as collective intelligence. We mashed up Open Application Programming Interface (OpenAPI) for scalable implementation of the system. Through experiments, we show that a user can build up his/her knowledge base, and by using this information, the system recommends to other users appropriate information that matches the user’s condition and profile through inference.     

位置服务社交网络用户行为相似性分析

基于位置的社交网络(LBSN)能够支持用户分享地理位置信息,网站中保存用户访问真实世界地理位置的记录构成用户的行为轨迹,但LBSN用户相似性的分析并没有从用户的地理位置轨迹上加以考虑。为此,提出基于划分层次,在不同的邻域半径下密度聚类的方法,探索基于位置的服务(LBS)平台上用户地理位置上相似性的度量。该方法在不同空间位置比例尺下观察用户访问各个聚类区域的次数,进而利用向量空间模型(VSM)计算用户在各个层级的相似性,最终以不同权重叠加各层级的用户相似性值,得出用户在地理空间行为上的相似性。基于国内某大型位置社交网站真实用户数据的实验结果表明,该方法能有效识别出访问地理位置相似的用户。     

基于个性化移动位置服务中自适应地图的研究

移动位置服务是一种新兴的无线增值业务,能够利用无线网络为移动用户提供基于位置的信息服务。个性化的移动位置服务是国内外研究的焦点,而自适应地图是其中最重要的方面之一。如何良好地进行地图的设计是本文研究的重点,由此提出了一种自适应地图形成模型,依据此模型可以较好地指导我们进行地图设计,进而满足移动位置服务中要求的易读性、动态性以及交互性。文章最后阐述了一种移动位置服务地图服务系统逻辑框架,并对其予以具体实现,验证了自适应地图服务系统的可行性和可用性。     

基于连续查询的用户轨迹k-匿名隐私保护算法

随着移动服务和移动网络的持续发展,基于LBS的连续查询服务被广泛应用。基于单点的K-匿名位置隐私保护算法已经不能满足连续查询下用户位置隐私需求。针对用户轨迹隐私保护提出新的保护方法,该方法采用不可信第三方中心匿名器,用户获取自己的真实位置后首先在客户端进行模糊处理,然后提交给第三方匿名器,第三方匿名器根据用户的隐私需求结合用户某时刻的真实位置信息生成虚假用户,然后根据历史数据生成虚假轨迹。为了进一步提高虚假轨迹与用户真实轨迹的相似性,该算法提出了虚假轨迹生成的两个约束条件：虚假轨迹距用户真实轨迹的距离约束和相似性约束。经大量实验证明,该算法与传统的不同时刻K-匿名算法相比,不仅可以满足连续查询的用户轨迹隐私保护而且可以满足基于快照的LBS用户位置隐私保护。     

基于Facade模式的移动定位服务系统平台的设计

移动定位服务(LBS)是一种新兴的无线增值业务,能够利用无线网络为移动用户提供基于位置的信息服务,具有巨大的市场潜力。LBS系统平台的好坏直接关系到商业运营的质量。本文从应用部署的实际出发,利用J2EE中的Facade模式来设计系统平台,从而可以避免接入层与业务层出现紧耦,使得系统各模块的开发更加高效、灵活。通过测试证明了该平台设计的可行性,能达到商业运营的基本要求。     

基于移动agent和元数据的位置服务信息查询*

为了有效访问基于位置服务(LBS)环境中的服务信息,提出了基于移动agent 和元数据的LBS信息查询新思路,给出了构建LBS信息查询系统的方法,并利用移动agent 开发平台Voyager和Java系列软件开发了一个分布式的LBS信息查询系统,实现了在LBS系统中信息的透明访问。     

Improving geographic information retrieval in spatial data infrastructures

In recent years, spatial data infrastructures (SDIs) have gained great popularity as a solution to facilitate interoperable access to geospatial data offered by different agencies. In order to enhance the data retrieval process, current infrastructures usually offer a catalog service. Nevertheless, such catalog services still have important limitations that make it difficult for users to find the geospatial data that they are interested in. Some current catalog drawbacks include the use of a single record to describe all the feature types offered by a service, the lack of formal means to describe the semantics of the underlying data, and the lack of an effective ranking metric to organize the results retrieved from a query. Aiming to overcome these limitations, this article proposes SESDI (Semantically-Enabled Spatial Data Infrastructures), which is framework that reuses techniques of classic information retrieval to improve geographic data retrieval in a SDI. Moreover, the framework proposes several ranking metrics to solve spatial, semantic, temporal and multidimensional queries.     

基于位置和时间的特定信息传输

介绍基于拥有移动手持设备用户的位置及时间信息,来为用户定制及发送用户要的特定信息的研究思路确定用户位置,接收用户请求,自动从服务器媒体信息库中获取特定信息并发送给用户,并且要保证传输的有效性,安全性,避免重复性。旅行信息提供统允许用户获得同时满足用户位置、时间用户资料和过程的标准信息。该系统把基于移动设备各种数据库和事件通知系统的观点整和到一个单一的框架中,并为LBS系统开发作参考。     

Recommendation of location-based services based on composite measures of trust degree

As more and more users use the mobile terminals of high computing power, the location-based services (LBS) recommendations for mobile users have become an important and interesting topic. Mobile users are eager to get their interested and reliable services quickly. A considerable number of research works have been dedicated to service recommendation based on users’ preferences and locations. In this paper, we study the credibility of recommended services, and propose a set of composite measures on how to provide more reliable services. We further propose the trustworthy Skyline of LBS recommendation in terms of the trust degree based on the newly introduced composite measures to achieve more credibility to provide recommendation services. Experimental results show that our method can recommend desired and trusted services to users.     

移动位置服务应用平台研究与开发

提出了一种移动位置服务的体系结构,开发、设计了其中的关键部件移动位置服务网关和位置服务应用平台,给出了实现位置服务的应用程序设计流程。经过实际测试表明,系统能够应用于移动通信网络为用户提供位置服务。     

面向移动用户的城市空间信息服务平台

随着移动通信技术的发展,基于位置的服务(LBS)越来越受到重视。然而传统的位置管理、空间位置信息服务系统(GIS)均是基于大型机/桌面机、宽带宽的应用环境;另一方面,在移动应用领域中的资源受限性使得传统的GIS构建方法并不适应于移动环境。文中旨在使移动用户能够在受限的移动应用环境中能和桌面用户一样使用城市空间信息服务。主要论述了在服务器端所要做的工作,并从多源数据的集成、空间数据的压缩方法及服务平台的框架结构等三个方面进行了论述。提出了基于WebServices的数据集成、基于小波的矢量地图压缩和JPEG2000的珊格地图压缩方法,并给出具体框架体系例子。文中所涉及到的框架体系原型在实际环境下运行良好,达到了预期的目的。     

基于Storm的连续范围查询优化技术

移动大数据环境下,传统基于位置服务LBS技术面临来自系统扩展性、性能等方面的挑战。首先针对LBS应用的特点,提出了基于Storm的查询框架。然后结合基于Storm的LBS查询框架,设计并实现了并行连续范围查询算法,优化查询性能。针对分布式环境中的一致性问题,设计使用基于ZooKeeper的分布式锁服务,保证查询结果的正确性。进一步,针对基于Storm并行连续范围查询算法中存在访问数据库开销较大的问题,提出了基于TimeCacheMap的缓存优化算法及两种缓存策略,减少了访问数据库的开销,提高了查询效率。     

基于LBS的“急速药递”小程序管理系统的设计与实现

为给紧急需要某种药物且意识清醒的用户快速提供药品,研究基于地理位置的药品求助和响应救助具有重要意义。在分析了用户急需某种药品但是无法获得时的需求,设计了基于用户地理位置(LBS)的“急速药递”小程序管理系统,对系统的紧急求助、消息推送、地图显示和基本信息填报等功能模块进行分析与设计,主要运用位置标记的方式展示求助信息、以消息推送的形式通知附近的志愿者或医药机构、地图显示药品求助信息、附近多位商家或志愿者响应求助并快速将药品送达,实现基于LBS的药品需求信息服务。系统采用云开发模式开发,描述了系统实现的关键技术,具有一定的实用价值。