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
The new Casper: query processing for location services without compromising privacy
TL;DR: 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.
BookDOI
Computing with Spatial Trajectories
Yu Zheng,Xiaofang Zhou +1 more
TL;DR: This book presents an overview on both fundamentals and the state-of-the-art research inspired by spatial trajectory data, as well as a special focus on trajectory pattern mining, spatio-temporal data mining and location-based social networks.
Proceedings ArticleDOI
Achieving k-anonymity in privacy-aware location-based services
TL;DR: Evaluation results show that the proposed DLS algorithm can significantly improve the privacy level in terms of entropy, and an enhanced-DLS algorithm that can enlarge the cloaking region while keeping similar privacy level as the DLS algorithms.
Proceedings ArticleDOI
Protecting Locations with Differential Privacy under Temporal Correlations
Yonghui Xiao,Li Xiong +1 more
TL;DR: A new definition, "δ-location set" based differential privacy, is proposed, to account for the temporal correlations in location data and a planar isotropic mechanism (PIM) for location perturbation, which is the first mechanism achieving the lower bound of differential privacy.
Journal ArticleDOI
Addressing the personalization-privacy paradox: an empirical assessment from a field experiment on smartphone users
TL;DR: Overall the proposed IT solution, which delivers a personalized service but avoids transmitting users' personal information to third parties, reduces users' perceptions that their information boundaries are being intruded upon, thus mitigating the personalization--privacy paradox and increasing both process and content gratification.
References
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Proceedings Article
Review - The R*-Tree: An Efficient and Robust Access Method for Points and Rectangles.
Proceedings ArticleDOI
Towards a Fail-Safe Approach to Secure Databases
A. D. Friedman,Lance J. Hoffman +1 more
TL;DR: This paper generalizes the definition of compromise to the discovery of the value of some (generally complex) property about a class of individuals and defines implied queries and imposes tests for safe response on both the query and implied queries.
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Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
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