计算机科学
基于位置的服务
信息隐私
互联网隐私
杠杆(统计)
隐私软件
计算机安全
计算机网络
机器学习
作者
Hira Rasheed,Rafidah Md Noor,Norjihan Abdul Ghani,Iftikhar Ahmad
摘要
Abstract Location‐based services (LBS) leverage the geographical information of a device to deliver information, entertainment, and other services tailored to the user's present location. LBS not only brings leisure to people's lives but also raises concerns about users' privacy. Consequently, location privacy protection has captured the attention of researchers owing to the increased adoption of location‐based services and the potential privacy issues faced by users. Along with location privacy, query privacy is also a crucial privacy concern that incurs possible damage to individual privacy and even to users' safety. Preserving location privacy only secures the user's current location but protection of query privacy guarantees the security of the user's future possible location. However, to the best of our knowledge, none of the relevant studies realized the significance of query privacy. This review paper provides an overview of LBS and its components, classifying the LBS based on: granularity, number of queries, initiator, and range. We investigated the threat model, vulnerabilities, and privacy attacks in LBS, reviewed the approaches used by the researchers to mitigate the location and query privacy threats, and evaluation metrics. We also analyzed the ability of current methods to implicitly/explicitly secure query privacy and the impact of recent technological progression on problem‐building and solution evolution. Finally, this paper concludes by identifying the open issues in the existing research and directions for future work.
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