Walk Alone and Be Fast: Trajectory Privacy-preserving in Complicated Environment

DOI

10.2991/cset-16.2016.51How to use a DOI?

Keywords

Privacy-preserving, trajectory data publication, maximum speed attack

Abstract

Trajectories are location samples ordered by sampling time, which is useful to multiple mobility-related applications. However, publication of these trajectories may cause serious personal privacy leakage. In this paper, we propose an approach called Walk Alone and Be Fast (WABF) to protect trajectory privacy against semantic location attack and maximum moving speed attack. WABF reduces the whole trajectories' exposure probability. At last, we conduct a set of comparative experimental studies on a real-world data set, the results show that WABF is effective and the information loss is much lower than k-anonymity methods.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Zheng Huo
AU  - Ping He
AU  - Ruoyan Wei
PY  - 2016/08
DA  - 2016/08
TI  - Walk Alone and Be Fast: Trajectory Privacy-preserving in Complicated Environment
BT  - Proceedings of the 2016 International Conference on Computer Science and Electronic Technology
PB  - Atlantis Press
SP  - 219
EP  - 222
SN  - 2352-538X
UR  - https://doi.org/10.2991/cset-16.2016.51
DO  - 10.2991/cset-16.2016.51
ID  - Huo2016/08
ER  -