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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Scalable and Robust Distributed Algorithms for Privacy-Preserving Applications
TL;DR: This dissertation studies scalable and robust distributed algorithms that guarantee user privacy when communicating with other parties either to solely exchange information or to participate in multi-party computations, which is one of the most generic problems in fault-tolerant computation.
Book ChapterDOI
Location Semantics Protection Based on Bayesian Inference
TL;DR: A novel method to model location semantics for user privacy using Bayesian inference and a cloaking region construction algorithm against the leakage of sensitive location semantics is introduced.
Journal ArticleDOI
A Survey on Location Privacy Preserving Techniques
TL;DR: The general framework of LBS system as well as the potential threats to LBS user are introduced and four categories including space cloaking, dummy-based method, private information retrieval, and differential privacy- based method are grouped.
Proceedings ArticleDOI
Poster: A Location-Privacy Approach for Continuous Queries
TL;DR: A novel Android App called MoveWithMe is proposed which automatically generates mocking locations and ensures that each trace looks like a trace of a real human and each trace is semantically different from the real user's trace.
Proceedings ArticleDOI
A New K-NN Query Processing Algorithm Enhancing Privacy Protection in Location-Based Services
Mi Young Jang,Jae-Woo Chang +1 more
TL;DR: A hybrid scheme to process an approximate k-Nearest Neighbor (k-NN) query by combining above two methods is proposed and it is shown that the hybrid scheme outperforms the existing work in terms of both query processing time and accuracy of the result set.
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