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
More filters
Book ChapterDOI
Reinforcement Learning Based Smart Data Agent for Location Privacy.
TL;DR: In this paper, a "privacy by design" approach is presented for location management on a smartphone that is based on the concept of smart data, where an on-the-go smart data agent learns the user's privacy policy in a manner that is interactive and adaptive, enabling it to adjust itself to changes in user preferences over time.
Journal ArticleDOI
Open Data Release and Privacy Concerns: Complexity in Mitigating Vulnerability with Controlled Perturbation
Shah Imran Alam,Ihtiram Raza Khan,Syed Imtiyaz Hassan,Farheen Siddiqui,M. Afshar Alam,Anil Kumar Mahto +5 more
TL;DR: It is found that anonymity-preserving algorithms did a substantial job to protect privacy in the release of the structured microdata and contradicts the general assumptions with a specific set of evaluation parameters for data quality assessment.
Journal ArticleDOI
An Efficient Location Based Anonymous Secure Routing Protocol for Mobile Ad hoc Network
TL;DR: This work proposes an efficient approach for providing security for data and network and proves that packet delivery ratio is enhanced to a larger extent, thereby maintaining a trade-off between efficiency and security.
Posted Content
Designing a Location Trace Anonymization Contest.
Takao Murakami,Hiromi Arai,Koki Hamada,Takuma Hatano,Makoto Iguchi,Hiroaki Kikuchi,Atsushi Kuromasa,Hiroshi Nakagawa,Yuichi Nakamura,Kenshiro Nishiyama,Ryo Nojima,Hidenobu Oguri,Chiemi Watanabe,Akira Yamada,Takayasu Yamaguchi,Yuji Yamaoka +15 more
TL;DR: In this article, a location trace anonymization contest was held to evaluate both the re-identification risk and trace inference risk, and analyzes the relation between the two risks, and also show that reidentification alone is insufficient as a privacy risk and that trace inference should be added as an additional risk.
References
More filters
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.
Related Papers (5)
Anonymous Usage of Location-Based Services Through Spatial and Temporal Cloaking
Marco Gruteser,Dirk Grunwald +1 more