Toward collective privacy using coordinative path planning

Abstract

With the increasing importance of location based services in people's daily lives, location related privacy becomes a critical issue. Most of the current solutions protect users' privacy by cloaking users' exact positions when they invoke requests on the location based service. In this paper, we tackle the location privacy problem in navigation applications in a different way. Based on the trusted third-party architecture and the k-anonymity criterion, we propose a coordinative path planning algorithm for collective privacy. The novelty resides on two folds. One fold is from the predication perspective rather than the current solutions' focusing on on-site users. A user would be recommended a privacy-preserving path when he sends a navigation request. Another fold is by intentionally collective privacy rather than the traditionally independent calculation. The planned path for each user would be adjusted such that a set of users could be provided more privacy without degrading each user's privacy. To evaluate the proposed solution, we perform a set of experiments on both synthesis data and practical data. The experimental results show the efficiency and effectiveness of our method. © 2014 Springer International Publishing.