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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Journal ArticleDOI
Ensuring Privacy in Location-Based Services: An Approach Based on Opacity Enforcement
TL;DR: This work synthesizes suitable insertion functions that insert fake queries into the cloaking query sequences and design an optimal insertion function that introduces minimum average number of fake queries to enforce location privacy.
Proceedings ArticleDOI
Private Buddy Search: Enabling Private Spatial Queries in Social Networks
Ali Khoshgozaran,Cyrus Shahabi +1 more
TL;DR: This work proposes Private Buddy Search (PBS), a framework to enable private evaluation of spatial queries predominantly used in social networks, without compromising sensitive information about its users, and shows that PBS enjoys both scalability and privacy.
Book ChapterDOI
Approximate Evaluation of Range Nearest Neighbor Queries with Quality Guarantee
TL;DR: This work proposes an approximate range NN query processing algorithm that is scalable and effectively reduces query response time while providing approximate query answers that satisfy the user specified approximation tolerance level.
Book ChapterDOI
A Hybrid Technique for Private Location-Based Queries with Database Protection
TL;DR: A hybrid, two-step approach to private location-based queries, which provides protection for both the users and the database, and clearly outperforms the pure-PIR approach in terms of computational and communication overhead.
Book ChapterDOI
Quality aware privacy protection for location-based services
TL;DR: This paper develops an efficient directed-graph based cloaking algorithm to achieve both high-quality location anonymity and identifier anonymity and introduces an option of using dummy locations to achieve a 100% cloaking success rate at the cost of communication overhead.
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