Privacy-preserving Publishing of a Pseudonym-based Trajectory Location Data Set (Extended Abstract)

スポンサーリンク

概要

• 論文の詳細を見る

• Anonymization is a common technique for publishing a location data set in a privacy-preserving way. However, such an anonymized data set lacks user trajectory information, which could be beneficial to many location-based analytic services. In this paper, we present a dynamic pseudonym scheme for constructing alternate possible paths for mobile users that protects their location privacy. We introduce a formal definition of location privacy for pseudonym-based location data sets and develop a polynomial-time verification algorithm for determining whether each user in a given location data set has a sufficient number of possible paths to disguise his/her true movements. We also provide the correctness proof of the algorithm.

• 2013-03-07

著者

• Kazuhiro Minami

  National Institute Of Informatics

• Ken Mano

  Ntt Communication Science Laboratories Ntt Corporation

• Kazuhiro Minami

  Institute of Statistical Mathematics

• Hiroshi Maruyam

  Institute of Statistical Mathematics

関連論文

• Preventing Denial-of-request Inference Attacks in Location-sharing Services
• Protecting Location Privacy with K-Confusing Paths Based on Dynamic Pseudonyms
• Protecting Location Privacy with K-Confusing Paths Based on Dynamic Pseudonyms
• Privacy-preserving Publishing of a Pseudonym-based Trajectory Location Data Set (Extended Abstract)
• Privacy-preserving Publishing of a Pseudonym-based Trajectory Location Data Set (Extended Abstract)

スポンサーリンク