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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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Patent

Location retargeting system for online advertising

TL;DR: In this paper, a system for retargeting customers is based on a mobile device query initiated by a user, and includes a query module configured to receive a query with geographical information from the mobile device and determine geographical identifiers of the mobile devices.
Proceedings ArticleDOI

Differentially Private Trajectory Analysis for Points-of-Interest Recommendation

TL;DR: This paper proposes a differentially private trajectory analysis algorithm for points-of-interest recommendation to users that aims at maximizing the accuracy of the recommendation results while protecting the privacy of the exposed trajectories with differential privacy guarantees.
Journal ArticleDOI

A network aware privacy model for online requests in trajectory data

TL;DR: This work proposes a privacy model for online user requests on trajectory data in location based services by utilizing an underlying network of user movement that reconstructs the user movement from a series of independent location updates.
Journal ArticleDOI

HilAnchor: Location Privacy Protection in the Presence of Users’ Preferences

TL;DR: This paper proposes a privacy protection solution to allow users' preferences in the fundamental query of k nearest neighbors (kNN), and particularly, users are permitted to choose privacy preferences by specifying minimum inferred region via Hilbert curve based transformation.
Proceedings ArticleDOI

Protecting user anonymity in location-based services with fragmented cloaking region

TL;DR: A novel framework is proposed, where the traditional single cloaking region (CR) is split into multiple sub CRs, and the area of the CR is decreased, resulting in improved accuracy of the candidate results returned from the LBS provider as well as reduced transmission overhead.
References
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Journal ArticleDOI

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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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