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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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Dissertation

Personalising privacy contraints in Generalization-based Anonymization Models

Axel Michel
TL;DR: This work proposes a novel concept of personalized anonymisation based on data generalization and user empowerment to push personalized privacy guarantees in the processing of database queries so that individuals can disclose different amounts of information depending on their own perception of the risk.
Journal ArticleDOI

Privacy-Enhancing Queries in Personalized Search with Untrusted Service Providers

TL;DR: The aim here is to protect user privacy by filtering out the sensitive information exposed from a user's query input at the system level by introducing the concept of query generalizer, a middleware that takes a query for personalized search, modifies the query to hide user's sensitive personal information adaptively depending on the user's privacy policy, and then forwards the modified query to the service provider.
Journal ArticleDOI

Privacy disclosure risk: smartphone user guide

TL;DR: This paper presents a preliminary study on privacy risks in smartphone environment from individual user perspective and provides and discusses a guideline to deal with privacy risk incident on smartphone.
Journal ArticleDOI

Maintaining anonymity using -privacy

TL;DR: A new model for privacy protection is introduced, heuristic defence techniques to protect users’ privacy from such attacks are provided, and the results of experiments performed to evaluate the heuristics are presented.
DissertationDOI

Novel techniques for location-cloaked applications

TL;DR: This research investigates two problems, and proposes a suite of noval techniques including query decomposition, scheduling, and personalized air indexing integrated into a single unified platform that is capable of handling various types of 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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