Journal ArticleDOI
Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms
Bugra Gedik,Ling Liu +1 more
TLDR
A scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs is described, including the development of a personalized location anonymization model and a suite of location perturbation algorithms.Abstract:
Continued advances in mobile networks and positioning technologies have created a strong market push for location-based applications. Examples include location-aware emergency response, location-based advertisement, and location-based entertainment. An important challenge in the wide deployment of location-based services (LBSs) is the privacy-aware management of location information, providing safeguards for location privacy of mobile clients against vulnerabilities for abuse. This paper describes a scalable architecture for protecting the location privacy from various privacy threats resulting from uncontrolled usage of LBSs. This architecture includes the development of a personalized location anonymization model and a suite of location perturbation algorithms. A unique characteristic of our location privacy architecture is the use of a flexible privacy personalization framework to support location k-anonymity for a wide range of mobile clients with context-sensitive privacy requirements. This framework enables each mobile client to specify the minimum level of anonymity that it desires and the maximum temporal and spatial tolerances that it is willing to accept when requesting k-anonymity-preserving LBSs. We devise an efficient message perturbation engine to implement the proposed location privacy framework. The prototype that we develop is designed to be run by the anonymity server on a trusted platform and performs location anonymization on LBS request messages of mobile clients such as identity removal and spatio-temporal cloaking of the location information. We study the effectiveness of our location cloaking algorithms under various conditions by using realistic location data that is synthetically generated from real road maps and traffic volume data. Our experiments show that the personalized location k-anonymity model, together with our location perturbation engine, can achieve high resilience to location privacy threats without introducing any significant performance penalty.read more
Citations
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Proceedings ArticleDOI
A Clustering-Based Location Privacy Protection Scheme for Pervasive Computing
TL;DR: Privacy analysis shows that the proposed approach can achieve high resilience to location privacy threats and provide more privacy than users expect, and complexity analysis shows clusters can be adjusted in real time as mobile users join or leave.
Patent
Method and apparatus for authenticating location-based services without compromising location privacy
Haibo Hu,Jianliang Xu,Qian Chen +2 more
TL;DR: In this article, a method and an apparatus for authenticating location-based services without compromising location privacy is presented, which comprises a comprehensive solution that preserves unconditional location privacy when authenticating either range queries using three authentication schemes for R-tree and grid-file index, together with two optimization techniques, or k-nearest neighbor queries using two authentication schemes.
Proceedings ArticleDOI
Location Obfuscation for Location Data Privacy
TL;DR: This work shows that user privacy can be maintained without affecting LBSs results, and without incurring significant overheads.
Proceedings ArticleDOI
k-DLCA: An efficient approach for location privacy preservation in location-based services
TL;DR: This paper proposes an efficient k-anonymity based Dummy Location and divided Circular Area (k-DLCA) approach to protect the user's location privacy and shows that the k-D LCA algorithm can resist the attacks from adversaries, and has a low probability of exposing the users' real location.
Book ChapterDOI
Privacy Preservation over Untrusted Mobile Networks
TL;DR: This chapter presents a survey of existing state-of-the-art protection mechanisms and their challenges when deployed in the context of wired and wireless networks, and presents a new proposal to ensure private communication in the contexts of hybrid mobile networks, which integrate wired, wireless and cellular technologies.
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
The R*-tree: an efficient and robust access method for points and rectangles
TL;DR: The R*-tree is designed which incorporates a combined optimization of area, margin and overlap of each enclosing rectangle in the directory which clearly outperforms the existing R-tree variants.
Journal ArticleDOI
The active badge location system
TL;DR: A novel system for the location of people in an office environment is described, where members of staff wear badges that transmit signals providing information about their location to a centralized location service, through a network of sensors.
Proceedings ArticleDOI
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Marco Gruteser,Dirk Grunwald +1 more
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.
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