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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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Journal ArticleDOI

Mobile participatory sensing with strong privacy guarantees using secure probes

TL;DR: PAMPAS is presented, a privacy-aware mobile distributed system for efficient data aggregation in MPS, and an enhanced version of the protocol, named PAMPAS + , makes the system robust even against advanced hardware attacks on the SPs.
Book ChapterDOI

User Personalized Location k Anonymity Privacy Protection Scheme with Controllable Service Quality

TL;DR: Wang et al. as mentioned in this paper proposed a user personalized location k anonymity privacy protection scheme with controllable service quality, where the quality of service is quantified according to service similarity, and both it and the degree of privacy protection are used as user-controllable parameters, which meet the user's more personalized privacy protection needs.
Book ChapterDOI

VAT: A Velocity-Aware Trajectory Privacy Preservation Scheme for IoT Searching

TL;DR: A type of trajectory privacy attack named “Velocity Inference Attack” is introduced, and it is shown that the preservation schemes which left out to protect the velocity information will fail to resist this attack.
Journal ArticleDOI

Location-Query-Privacy and Safety Cloaking Schemes for Continuous Location-Based Services

TL;DR: Two batch techniques to provide location privacy, location safety, and query privacy in an environment that considers a continuous LBS are proposed and extensive experimentation shows that both techniques are cost-effective and scalable solutions that offer unified location Privacy, query privacy, and location safety protection for many mobile users.
Dissertation

Scalable Spatial Predictive Query Processing for Moving Objects

TL;DR: This dissertation aims to provide a history of web exceptionalism from 1989 to 2002, a period chosen in order to explore its roots as well as specific cases up to and including the year in which descriptions of “Web 2.0” began to circulate.
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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