Preserving User Location Privacy in Mobile Data Management Infrastructures
Preserving User Location Privacy in Mobile Data Management Infrastructures
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DOI：
10.1007/11957454_23
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发表时间：
2006-06
期刊：
影响因子：
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通讯作者：
Reynold Cheng;Yu Zhang;E. Bertino;Sunil Prabhakar
中科院分区：
文献类型：
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作者：
Reynold Cheng;Yu Zhang;E. Bertino;Sunil Prabhakar
基于位置的服务，如查找最近的加油站，需要用户提供他们的位置信息。然而，用户的位置可以在未经她同意或不知情的情况下被跟踪。已经提出了降低发送到服务器的位置数据的空间和时间分辨率作为解决方案。虽然这种技术在保护隐私方面是有效的，但它可能是矫枉过正的，并且所需服务的质量可能会受到严重影响。在本文中，我们提出了一个框架，可以控制不确定性，提供高质量和隐私保护的服务，并探讨如何在GPS和蜂窝网络系统中实现这样的框架。基于这个框架，我们提出了一个数据模型，以增加位置数据的不确定性，并提出不精确的查询，隐藏的查询发布者的位置，并产生概率结果。我们调查的评价和质量方面的范围查询。我们还提供了新的方法来保护我们的解决方案免受恶意跟踪。实验进行了检查我们的方法的有效性。
Location-based services, such as finding the nearest gas station, require users to supply their location information. However, a user’s location can be tracked without her consent or knowledge. Lowering the spatial and temporal resolution of location data sent to the server has been proposed as a solution. Although this technique is effective in protecting privacy, it may be overkill and the quality of desired services can be severely affected. In this paper, we suggest a framework where uncertainty can be controlled to provide high quality and privacy-preserving services, and investigate how such a framework can be realized in the GPS and cellular network systems. Based on this framework, we suggest a data model to augment uncertainty to location data, and propose imprecise queries that hide the location of the query issuer and yields probabilistic results. We investigate the evaluation and quality aspects for a range query. We also provide novel methods to protect our solutions against trajectory-tracing. Experiments are conducted to examine the effectiveness of our approaches.