Protecting Location Privacy with Personalized k-Anonymity: Architecture and Algorithms

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 requirem...

• Type: Journal
• Year: 2008
• Where: TMC
• Authors: Bugra Gedik, Ling Liu
• Keywords: Location Perturbation, Location Privacy, Mobile Clients, TMC 2008