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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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Differential Privacy Models for Location-Based Services

TL;DR: This paper considers the adaptation of differential privacy to the context of location-based services LBSs, which personalize the information provided to a user based on his current position, and introduces and analyzes one of these models, the D, e-location privacy, which is directly inspired from the standard differential privacy model.
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Outsourcing Search Services on Private Spatial Data

TL;DR: This work transforms location data before uploading to achieve spatial transformations that re-distribute locations in space and a transformation that employs cryptographic techniques that achieve different tradeoffs between query efficiency and data security.
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A distributed spatial index for error-prone wireless data broadcast

TL;DR: DSI is very resilient to the error-prone wireless communication environment because interrupted search operations based on DSI can be resumed easily and supports search algorithms for classical location-based queries such as window queries and kNN queries in both of the snapshot and continuous query modes.
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UV-diagram: A Voronoi diagram for uncertain data

TL;DR: This paper proposes the Uncertain-Voronoi Diagram, an alternative representation for UV-partitions, and develops an adaptive index for the UV-diagram, which can be constructed in polynomial time.
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Application of Local Differential Privacy to Collection of Indoor Positioning Data

TL;DR: Experimental results with both synthetic and actual data sets demonstrate that LDP is well applicable to the collection of indoor positioning data for the purpose of inferring population statistics.
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.
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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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