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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

UV-diagram: a voronoi diagram for uncertain spatial databases

TL;DR: The Uncertain-Voronoi diagram (or UV-diagram), which divides the data space into disjoint “UV-partitions”, and uses a set of UV-cells to design the UV-index, which supports different queries, and can be constructed in polynomial time.
Proceedings ArticleDOI

Pattern Matching over Cloaked Time Series

TL;DR: This paper formalizes such similarity search problem over the cloaked time series, and proposes a novel approach to index the cloaked series, which can facilitate the similarity query.
Journal ArticleDOI

Location Privacy for Mobile Crowd Sensing through Population Mapping

TL;DR: This work proposes and evaluates a novel spatiotemporal blurring mechanism based on tessellation and clustering to protect users' privacy against the system while reporting context, and employs a notion of probabilistic k-anonymity.
Journal ArticleDOI

Inferring Social Strength from Spatiotemporal Data

TL;DR: An entropy-based model (EBM) is proposed that not only infers social connections but also estimates the strength of social connections by analyzing people’s co-occurrences in space and time and develops a parallel implementation of the algorithm using MapReduce to create a scalable and efficient solution for online applications.
Journal ArticleDOI

Quantifying and Protecting Location Privacy

Reza Shokri
TL;DR: This thesis constructs an analytic framework for location privacy that formalizes users' mobility model, their access pattern to location-based services, and their privacy and service quality requirements, and model location-privacy preserving mechanisms as probabilistic functions that obfuscate users' (location and identity) information before being shared with location- based services.
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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