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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Journal ArticleDOI
An incentive mechanism for K-anonymity in LBS privacy protection based on credit mechanism
TL;DR: An incentive mechanism based on credit is introduced into the distributed K-anonymity, and only providing assistance to the others, a user can gain and accumulate his credit and avoid the security issue resulting from its breach.
Journal ArticleDOI
Exploring Extant and Emerging Issues in Anonymous Networks: A Taxonomy and Survey of Protocols and Metrics
TL;DR: A novel cubic taxonomy is developed to facilitate the systematic definition and classification of anonymity in anonymous communications networks and surveys anonymous protocols and quantifiable metrics essential for any entity determined to assure anonymity and preserve privacy in cyberspace against an adversary.
Proceedings ArticleDOI
Preserving Anonymity of Recurrent Location-Based Queries
TL;DR: It is shown that the presence of multiple concurrent requests, the repetition of similar requests by the same issuers, and the distribution of different service parameters in the requests can significantly affect the level of privacy obtained by current anonymity-based techniques.
Proceedings ArticleDOI
Measuring query privacy in location-based services
Xihui Chen,Jun Pang +1 more
TL;DR: New metrics to measure users' query privacy taking into account user profiles are proposed and spatial generalisation algorithms to compute regions satisfying users' privacy requirements expressed in these metrics are designed.
Journal ArticleDOI
Cognitive Approach for Location Privacy Protection
TL;DR: This paper introduces a heterogeneous multi-server architecture that cuts off the direct connection between the LBS queries and the query issuers, and an auction-based incentive mechanism guaranteed user participation, which is critical for the success of the proposed architecture.
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
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The active badge location system
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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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