Generating Perturbations with Hilbert Curves and Differential Privacy for Location Privacy

DOI

10.2991/mecae-17.2017.17How to use a DOI?

Keywords

Location-based Services, k-anonymity, Differential privacy, Privacy protection.

Abstract

Location privacy protection method of Location-based Services (LBS) system mostly depends on the trusted third party anonymity server. When the attacker has sufficient background knowledge, it is proved that Location privacy can't be adequately protected by k-anonymity based on location obfuscation enforced using cloaking regions. But perturbation-based mechanisms with differential privacy have been proven effective to defend attackers with any background knowledge. In this paper, k-anonymity and differential privacy are used to generate a perturbation, according to that the incremental nearest neighbor query is enforced, so as to achieve the purpose of LBS privacy protection. Experiments are presented to demonstrate how perturbation-based mechanisms provide a well-balanced tradeoff between privacy and service accuracy.

Copyright

© 2017, 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  - Na Wang
AU  - Haiyang Yu
PY  - 2017/03
DA  - 2017/03
TI  - Generating Perturbations with Hilbert Curves and Differential Privacy for Location Privacy
BT  - Proceedings of the 2017 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2017)
PB  - Atlantis Press
SP  - 95
EP  - 100
SN  - 2352-5401
UR  - https://doi.org/10.2991/mecae-17.2017.17
DO  - 10.2991/mecae-17.2017.17
ID  - Wang2017/03
ER  -