保护隐私和Content-Protecting基于位置查询

Anandababu一¹,年代。Brintha Rajakumari²,Dr.C.Nalini³

打开学生,计算机科学与工程系,Bharath大学印度钦奈
计算机科学与工程系,副教授Bharath大学印度钦奈
计算机科学与工程系,教授Bharath大学印度钦奈

文摘

本文提出一个解决方案的一个基于地理位置的查询问题。这个问题定义如下:(i)用户想要查询数据库位置的数据,称为的兴趣点(POI),并且不愿透露他/她的位置服务器由于隐私问题;(2)位置数据的所有者,即位置服务器,并不想简单地将其数据分发给所有用户。服务器位置的欲望有一些控制其数据,由于数据是其资产。基于位置查询解决方案,采用两个协议,允许用户私下决定和获取位置数据。第一步是为用户私下决定他/她的位置在公共电网使用的传输。无视转移用于实现双方更安全的解决方案。第二步涉及私人信息检索交互检索记录,沟通效率高。目前的解决方案是有效和实用的,在许多场景。本文包括一个工作原型的结果说明我们的协议的效率。

关键字

基于内容的挖掘、基于位置的服务,NN(近邻),POI的兴趣点,有效区域(VR)

介绍

基于位置的服务(lbs),也称为位置相关的信息服务(LDISs),被认为是一个重要的上下文感知应用程序在普适计算环境中。空间查询是最重要的一个磅。根据空间约束、空间查询可以分为几个类别包括最近邻(NN)查询和窗口查询。一个神经网络查询是找到最近的数据对象对的位置查询发布(称为神经网络查询的查询位置)。例如,用户可能发射一个神经网络查询像“显示最近的咖啡店对我的当前位置。”另一方面,一个窗口查询找到的所有对象在一个特定的窗口框架。一个窗口查询”显示所有餐厅在我的汽车导航窗口。“一般来说,一个移动客户端不断发射空间查询,直到客户获得一个满意的答复。例如,查询”给我的最近的酒店关于多美当前位置”不断提交在一个移动的汽车,找到一个理想的酒店。天真的回答连续空间查询方法是提交一个新的查询时查询位置变化。天真的方法能够提供正确的结果,但它带来了以下问题:高功率消耗。 The power consumption of a mobile device is high since the mobile device keeps submitting queries to the LBS server. Heavy server load. A continuous query usually consists of a number of queries to the LBS server, thereby increasing the load on the LBS server. Fortunately, in the real world, the queries of a continuous query usually exhibit spatial locality. Thus, caching the query result and the corresponding valid region (VR) in the client side cache was proposed to mitigate the above problems. The valid region, also known as the valid scope, of a query is the region where the answer of the query remains valid. Subsequent queries can be avoided as long as the client is in the valid region.

在本文中,我们集中在有效位置相关查询和处理,尤其是一个子类的查询称为移动加权(NN)搜索。移动发行的神经网络搜索是移动客户机获取固定的服务对象的用户。1It is an important function for LBSs, but the implementation is difficult since the clients are mobile and queries must be answered based on the clients’ current locations. If a client keeps moving after it issued a query, the query result would continue to change in accordance with the client’s movement. As such, it is difficult to obtain results which are accurate with respect to the position pat which the user receives them. Despite the fact that LBSs open up new research opportunities, most of the on-going research work still concentrates on traditional queries which return answers independent to the locations of the query issuers. In other words, each data object has only one set of attribute values in the server. If a client caches a local copy of the data to improve performance, the cached data become invalid only when the corresponding copy in the server is updated. As for location-dependent queries, a data object usually has multiple sets of attribute values, each of which is valid only when the client is located within a specific region. While mobile data caching and invalidation for locationindependent queries has been actively pursued in the mobile computing research community, very few work had been done on indexing and query processing techniques for location-dependent queries.

二世。相关工作

高速无线网络的出现和移动设备的普及推动了移动计算的发展。与传统计算模式相比,移动计算使客户能够有无限制的流动性,同时保持网络连接。用户移动的能力,确定自己的位置开辟了一种新的信息服务,称为依赖所在信息服务(LDISs),产生一个查询的答案根据客户端发出查询的位置。移动LDISs的例子包括最近的对象搜索(例如,找最近的餐厅)和本地信息访问(例如,当地交通,新闻,和景点。依赖所在的空间属性数据为数据缓存的研究引入了新的问题。首先,查询缓存的结果(例如,最近的餐馆)可能成为无效当客户端从一个位置移动到另一个地方。维护缓存数据的有效性,当客户端更改位置称为依赖所在缓存失效。第二,在客户端缓存替换策略必须考虑有效的大小范围p(以下简称有效范围区域)的缓存值。一个数据值的有效范围的地理区域内被定义为数据值是有效的。当一个数据值的有效范围很大,客户端问题的机会相同的查询在有效的范围内,从而生成缓存命中,也大。 As such, the cache replacement policy should try to retain the data value with pa larger valid scope area in the cache. [1]

由于增加了手机用户的需求,基于位置的服务(lbs)近年来获得了广泛的关注。查询定位服务的例子包括“发现从我的当前位置最近的加油站”,“找1公里半径内的所有电影院”,“公交车会经过我在接下来的10分钟吗?”等等。数据对象在前两个例子是静止的,在最后一个例子是移动。在本文中,我们专注于相对静态的查询手机用户发布的数据对象,因为它们是最常见的查询在磅。移动客户的运动为依赖所在查询处理提供了许多新的研究问题有几个技术问题涉及一磅的实现,其中包括定位移动用户的位置,追踪和预测运动,有效地处理查询,和边界位置错误。[2]

考虑一个计算环境与大量的位置感知移动对象。我们希望检索移动对象在一组用户定义的空间区域和持续监测这些窗口在一个时期的人口。在这篇文章中,我们参照区段监测等连续查询的查询。有效处理区段监测查询可能会使许多有用的应用程序。同样,我们可能需要轨道交通条件销一些区域和该地区派遣更多的警察如果车辆内部的数量超过某个阈值。在这样的应用程序,它是非常可取的,有时候关键时提供准确的结果和实时更新它们销移动物体进入或离开感兴趣的区域。与传统的范围查询不同,一个区段监测查询是一个连续查询。保持活跃,直到终止由用户显式地。查询结果对象继续,相应地改变,需要不断更新。一个简单的计算范围监控查询策略是每个对象报告移动地位。 The server uses this information to identify the affected queries, and updates their results accordingly. This simple approach requires excessive location updates, and obviously is not scalable. Each location update consists of two expenses - mobile communication cost and server processing cost. If a battery-powered object has to constantly report its location, the battery would be exhausted very quickly. It is well-known that sending a wireless message consumes substantially more energy than running simple procedures . [3] . Mobile devices with computational, storage, and wireless communication capabilities (such as PDAs) are becoming increasingly popular. At the same time, the technology behind positioning systems is constantly evolving, enabling the integration of low cost GPS devices in any portable unit. Consequently, new mobile computing applications are expected to emerge, allowing users to issue location-dependent queries in a ubiquitous manner. Consider, for instance, a user (mobile client) in an unfamiliar city, who would like to know the 10 closest restaurants. This is an instance of a k nearest neighbor (kNN) query, where the query point is the current location of the client and the set of data objects contains the city restaurants. Alternatively, the user may ask for all restaurants located within a certain distance, i.e., within 200 meters.[5] This is an instance of a range query. Spatial queries have been studied extensively in the past, and numerous algorithms exist (for processing snapshot queries on static data indexed by a spatial access method. Subsequent methods focused on moving queries (clients) and/or objects. The main idea is to return some additional information (e.g., more NNs expiry time validity region that determines the lifespan of the result. Thus, a moving client needs to issue another query only after the current result expires. These methods focus on single query processing, make certain assumptions about object movement and do not include mechanisms for maintenance of the query results (i.e., when the result expires, a new query must be issued). Recent research considers continuous monitoring of multiple queries over arbitrarily moving objects. In this setting, there is a central server that monitors the locations of both objects and queries. The task of the server is to report and continuously update the query results as the clients and the objects move. As an example, consider that the data objects are vacant cabs and the clients are pedestrians that wish to know their k closest free taxis until they hire one. [6]As the reverse case, the queries may correspond to vacant cabs, and each free taxi driver wishes to be continuously informed about his/her k closest pedestrians. Several monitoring methods have been proposed, covering both range and kNN queries. Some of these methods assume that objects issue updates whenever they move, while others consider that data objects have some computational capabilities, so that they inform the server only when their movement influences some query.

三世。基于位置查询

在本文中,我们提出一个新颖的协议基于位置查询,主要由Ghinita性能改进的方法。像我们这样的协议,协议是根据两个阶段组织。在第一阶段,用户私下决定了他/她的位置在一个公共电网,对称密钥关联使用的传输的数据块的私人网格。在第二阶段,用户执行一个通信的高效的PIR,检索适当的私人网格块。这个块使用对称密钥解密获得在前面的阶段。我们的协议从而为用户和服务器提供了保护。用户是受保护的,因为服务器是无法确定他/她的位置。同样,服务器的数据保护,因为恶意用户只能解密的数据块与加密密钥通过PIR收购了在前面的阶段。换句话说,用户无法获得比他们支付更多的数据。我们还提供一个工作原型的结果显示我们的方法的效率。

3.1系统设计

提出了神经网络系统架构和窗口查询处理。系统架构包含三个部分:1)外部磅服务器,2)部署代理,和3)移动客户。磅服务器负责管理静态数据对象和回答查询提交的代理。注意,磅服务器可以使用任何索引结构(例如,r - tree或网格索引)来处理空间查询。伦敦商学院的服务器被认为不提供工具。每个部署代理监督一个服务区,为移动客户提供窗口查询的服务区域。每个基站作为一个中间继电器之间移动客户查询和查询结果和相关的代理。基站、代理和磅服务器通过有线网络连接。移动客户端维护一个缓存存储查询结果和相应的。当一个移动客户端有一个空间查询,移动设备首先检查当前位置是否在存储的结果。 If so, the stored result remains valid and the mobile device directly shows it to the client. Otherwise, the mobile device submits the query, which is received and then forwarded by the base station, to the proxy. For the received query, the proxy will return the query result as well as the corresponding EVR to the client.

3.2.1客户处理移动设备提交查询,然后转发收到的基站,代理。收到查询,代理将返回查询结果以及相应的应答客户端。移动设备提交查询代理的数量。大量的查询提交的移动客户端。

3.2.2代理模块:在这个模块构建代理服务器来估计将神经网络的基于神经网络的查询和EWVs窗口查询查询历史和可用的数据对象。磅服务器可以使用任何索引结构例如,r - tree或处理空间网格索引查询。代理维护对象缓存和两个索引结构:一个EVR-tree神经网络查询和网格索引的查询窗口。这两个指数结构共享对象缓存中的数据对象。

EVR-TREE代的神经网络

EVR-tree是r - tree(或其变体)组成的回过头,每个回过头裹在一个最小边界框(MBR)。一个将由该地区顶点数据对象和一个指向相应的对象缓存中的对象条目。当一个神经网络查询点位于一个EVR-tree回过头,代理从对象缓存中检索对应的对象来回答查询。

生成的网格单元窗口查询

网格细胞分为两类:完全缓存细胞和未细胞所有网格细胞是未初始化。代理是一个细胞时完全缓存细胞内的所有对象。相应的网格索引条目完全缓存单元缓存对象指针的对象缓存中的条目相关联的对象。完全缓存和未细胞的目的是实现存储对象分布,使有效代理创建EWVs窗口查询。当收到一个窗口查询,代理获得结果,创建相应的EWV检索对象存储在缓存完全细胞周围

3.2.3。服务器模块

磅服务器负责管理静态数据对象和回答查询提交的代理。

3.3算法

R树算法:R - Tree可以更高效的数据存储和搜索速度执行时间,尽管他们通常与给定数据存储系统的内部结构。r - tree树数据结构用于空间访问方法,即。,for indexing multi-dimensional information such as geographical coordinates, rectangles or polygons. A common real-world usage for an R-tree might be to store spatial objects such as restaurant locations or the polygons that typical maps are made of: streets, buildings, outlines of lakes, coastlines, Grid index algorithm: The individual cells of a grid system can also be useful as units of aggregation, for example as a precursor to data analysis, presentation, mapping, etc. A grid index is a used for spatial indexing purposes. A wide variety of such grids have been proposed or are currently in use, including grids based on "square" or "rectangular" cells, triangular grids or meshes, hexagonal grids, grids based on diamond-shaped cells, and possibly more. The range is broad and the possibilities are expanding. Melkman’s algorithm: The Melkman’s algorithm to compute the convex polygon of the updated EVR to remove the unnecessary vertices and achieve a larger region size. The convex polygon serves as the final updated EVR .

四、实验设置和结果

本文是在。net框架中实现和后端使用sql。

诉的结论

在本文中,我们提出了一个基于位置查询解决方案,采用两个协议,允许用户私下决定和获取位置数据。第一步是为用户私下决定他/她的位置在公共电网使用的传输。第二步涉及私人信息检索交互检索记录,沟通效率高。我们协议的性能进行了分析,发现它比的计算和通信的高效解决方案,Ghinita et al .,这是最新的解决方案。我们实现了一个软件原型使用桌面机和一个移动设备。软件原型表明我们的协议是在切实可行的范围内。未来的工作将包括测试协议在许多不同的移动设备上。我们提供的移动的结果可能不同于其他移动设备和软件环境。同时,我们需要减少的开销主要测试中使用私人信息检索基础协议。此外,有关LS问题提供误导性的数据到客户端也是有趣的。 Privacy preserving reputation techniques seem a suitable approach to address such problem. A possible solution could integrate methods from. Once suitable strong solutions exist for the general case, they can be easily integrated into our approach.

引用

b . d . Lee郑,观测。李,“数据管理位置相关的信息服务,”IEEE普适计算,1卷,没有。3,页65 - 72,July-Sept。2002年。
郑,j .徐D.L.李,“缓存失效和替换策略位置无关的数据在移动环境中,它把“IEEE反式。电脑,15卷,。10日,第1153 - 1141页,2002年10月。
b .郑D.L.李,“处理”位置无关的查询在多单元无线环境中,它把Proc。第二个数据Eng ACM国际研讨会。无线和移动访问,2001年。
郑,j .徐观测。李,李D.L.”语义缓存和查询调度移动近邻搜索,“无线网络,10卷,没有。6日,第664 - 653页,2004年12月。
x高和a . Hurson”位置相关查询代理,“Proc.ACM国际协会应用计算,1120 - 1124年,2005页。
x高,j . Sustersic境Hurson,“窗口查询Processingwith代理缓存,”