Private Queries and Trajectory Anonymization: a Dual Perspective on Location Privacy
Private Queries and Trajectory Anonymization: a Dual Perspective on Location Privacy
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发表时间：
2009-04
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通讯作者：
G. Ghinita
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作者：
G. Ghinita
具有因特网连接性的移动的设备的出现（例如，Wi-Fi）和全球定位能力（例如，GPS）已经引发了基于位置的应用的广泛发展。例如，用户能够询问关于他们附近的兴趣点的查询。此外，用户可以充当移动的传感器来监测交通流量或空气污染水平。然而，这些应用程序要求用户披露他们的位置，这引起了严重的隐私问题。通过了解用户位置，恶意攻击者可以推断出敏感信息，例如其他生活方式或政治派别。保护位置隐私是成功部署基于位置的服务（LBS）的基本要求。目前，存在两种主要的LBS使用场景：在第一种场景中，用户向不可信的服务器发送基于位置的查询，隐私目标是保护查询用户的位置。在第二种设置中，诸如电话公司的可信实体收集大量位置数据（即，轨迹轨迹）并且希望发布它们用于数据挖掘（例如，缓解交通拥堵）。在这种情况下，防止对手将轨迹与用户身份相关联至关重要。在这篇调查论文中，我们从查询隐私和轨迹匿名的双重角度概述了位置隐私保护的最新技术。我们回顾了最突出的设计选择和技术解决方案，并强调了它们的相对优势和劣势。
The emergence of mobile devices with Internet connectivity (e.g., Wi-Fi) and global positioning capabilities (e.g., GPS) have triggered the widespread development of location-based applications. For instance, users are able to ask queries about points of interest in their proximity. Furthermore, users can act as mobile sensors to monitor traffic flow, or levels of air pollution. However, such applications require users to disclose their locations, which raises serious privacy concerns. With knowledge of user locations, a malicious attacker can infer sensitive information, such as alternative lifestyles or political affiliations. Preserving location privacy is an essential requirement towards the successful deployment of location-based services (LBS). Currently, two main LBS use scenarios exist: in the first one, users send location-based queries to an un-trusted server, and the privacy objective is to protect the location of the querying user. In the second setting, a trusted entity, such as a telephone company, gathers large amounts of location data (i.e., trajectory traces) and wishes to publish them for data mining (e.g., alleviating traffic congestion). In this case, it is crucial to prevent an adversary from associating trajectories to user identities. In this survey paper, we give an overview of the state-of-the-art in location privacy protection from the dual perspective of query privacy and trajectory anonymization. We review the most prominent design choices and technical solutions, and highlight their relative strengths and weaknesses.