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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Enhanced Internet Mobility and Privacy Using Public Cloud
TL;DR: This paper investigates the potential privacy risk of mobile Internet users and proposes a scalable system built on top of public cloud services that can hide mobile user’s network location and traffic from communication peers and creates a dynamic distributed proxy network for each mobile user to minimize performance overhead and operation cost.
Journal ArticleDOI
Enhanced Internet Mobility and Privacy Using Public Cloud
TL;DR: Wang et al. as mentioned in this paper investigated the potential privacy risk of mobile Internet users and proposed a scalable system built on top of public cloud services that can hide mobile user's network location and traffic from communication peers.
Book ChapterDOI
Security and Privacy Challenges for Big Data on Social Media
TL;DR: In the current electronic age, users create vast and enormous data every second, and it is growing at exponential rates as discussed by the authors. But it is difficult to process it effectively with the conventional data processing methods, and therefore, the computing infrastructure should be managed in such a way that can handle this Big Data.
Proceedings ArticleDOI
RPROB - a family of binomial-mix-based anonymous communication systems
TL;DR: Experimental evaluation shows that any instance of RPROB provides higher anonymity than APROB Channel with the same environment and users' behaviors (rate and number of sent messages), and because of the randomness provided by a binomial mix, an adversary cannot determine with certainty the probability of a user to be a sender of a delivered message in RPR OB system.
Journal ArticleDOI
A new distributed user-demand-driven location privacy protection scheme for mobile communication network
Jin Wang,Hao Wu,Yudian Liu +2 more
TL;DR: This paper constructs a distributed framework, in which location privacy protection is wholly occupied in server side and LBS provider is only dedicated to QoS-guarantee, and a user-defined weight parameter is introduced to ensure location privacy security without decreasing QoS.
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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