The new Casper: query processing for location services without compromising privacy
Mohamed F. Mokbel,Chi-Yin Chow,Walid G. Aref +2 more
- pp 763-774
TLDR
Zhang et al. as mentioned in this paper presented Casper1, a new framework in which mobile and stationary users can entertain location-based services without revealing their location information, which consists of two main components, the location anonymizer and the privacy-aware query processor.Abstract:
This paper tackles a major privacy concern in current location-based services where users have to continuously report their locations to the database server in order to obtain the service. For example, a user asking about the nearest gas station has to report her exact location. With untrusted servers, reporting the location information may lead to several privacy threats. In this paper, we present Casper1; a new framework in which mobile and stationary users can entertain location-based services without revealing their location information. Casper consists of two main components, the location anonymizer and the privacy-aware query processor. The location anonymizer blurs the users' exact location information into cloaked spatial regions based on user-specified privacy requirements. The privacy-aware query processor is embedded inside the location-based database server in order to deal with the cloaked spatial areas rather than the exact location information. Experimental results show that Casper achieves high quality location-based services while providing anonymity for both data and queries.read more
Citations
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A survey of computational location privacy
TL;DR: This is a literature survey of computational location privacy, meaning computation-based privacy mechanisms that treat location data as geometric information, which includes privacy-preserving algorithms like anonymity and obfuscation as well as privacy-breaking algorithms that exploit the geometric nature of the data.
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Preventing Location-Based Identity Inference in Anonymous Spatial Queries
TL;DR: This work proposes transformations based on the well-established K-anonymity concept to compute exact answers for range and nearest neighbor search, without revealing the query source.
Proceedings ArticleDOI
Personalized privacy preservation
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TL;DR: Wang et al. as discussed by the authors presented a new generalization framework based on the concept of personalized anonymity, which performs the minimum generalization for satisfying everybody's requirements, and thus retains the largest amount of information from the microdata.
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Computing with Spatial Trajectories
Yu Zheng,Xiaofang Zhou +1 more
TL;DR: This book presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks.
Proceedings ArticleDOI
A peer-to-peer spatial cloaking algorithm for anonymous location-based service
TL;DR: Experimental results show that the P2P spatial cloaking algorithm operated in the on-demand mode has lower communication cost and better quality of services than the proactive mode, but theOn-demand incurs longer response time.
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