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Open AccessProceedings ArticleDOI

The new Casper: query processing for location services without compromising privacy

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.

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Citations
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Proceedings ArticleDOI

Efficient Evaluation of k-Range Nearest Neighbor Queries in Road Networks

TL;DR: An efficient kRNN query processing algorithm that employs a shared execution approach to eliminate the redundant searching overhead, and provides a parameter that can be tuned to achieve a tradeoff between the query processing performance and the storage overhead, while guaranteeing the user’s exact k-nearest neighbors are included in the query answers.
Journal ArticleDOI

On Designing Satisfaction-Ratio-Aware Truthful Incentive Mechanisms for $k$ -Anonymity Location Privacy

TL;DR: This paper revisits the problem of stimulating users that are privacy-indifferent to participate in the anonymity set and providing k-anonymity location privacy for privacy-sensitive users and designs auction-based mechanisms that achieve higher satisfaction ratio than the existing work.
Posted Content

Shortest Path Computation with No Information Leakage

TL;DR: This paper aims at strong privacy, where the adversary learns nothing about the shortest path query, via established private information retrieval techniques, which are treated as black-box building blocks.
Journal ArticleDOI

Matching Anonymized and Obfuscated Time Series to Users’ Profiles

TL;DR: It is demonstrated that as the number of users in the network grows, the obfuscation-anonymization plane can be divided into two regions: in the first region, all users have perfect privacy; and, in the second region, no user has privacy.
Journal ArticleDOI

Range-Based Skyline Queries in Mobile Environments

TL;DR: Two novel algorithms are proposed: one is index-based (I- SKY) and the other is not based on any index (N-SKY), which develop efficient solutions for probabilistic and continuous range-based skyline queries.
References
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Journal ArticleDOI

k -anonymity: a model for protecting privacy

TL;DR: The solution provided in this paper includes a formal protection model named k-anonymity and a set of accompanying policies for deployment and examines re-identification attacks that can be realized on releases that adhere to k- anonymity unless accompanying policies are respected.
Proceedings ArticleDOI

Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking

TL;DR: A middleware architecture and algorithms that can be used by a centralized location broker service that adjusts the resolution of location information along spatial or temporal dimensions to meet specified anonymity constraints based on the entities who may be using location services within a given area.
Journal ArticleDOI

Protecting respondents identities in microdata release

TL;DR: This paper addresses the problem of releasing microdata while safeguarding the anonymity of respondents to which the data refer and introduces the concept of minimal generalization that captures the property of the release process not distorting the data more than needed to achieve k-anonymity.
Journal ArticleDOI

Achieving k -anonymity privacy protection using generalization and suppression

TL;DR: This paper provides a formal presentation of combining generalization and suppression to achieve k-anonymity and shows that Datafly can over distort data and µ-Argus can additionally fail to provide adequate protection.
Journal ArticleDOI

Location privacy in pervasive computing

TL;DR: The mix zone is introduced-a new construction inspired by anonymous communication techniques-together with metrics for assessing user anonymity, based on frequently changing pseudonyms.
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