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A hybrid location privacy protection scheme in big data environment

conference contribution
posted on 2017-07-01, 00:00 authored by Mohammad NosouhiMohammad Nosouhi, V V H Pham, Shui Yu, Yong XiangYong Xiang, Matthew Warren
© 2017 IEEE. Location privacy has become a significant challenge of big data. Particularly, by the advantage of big data handling tools availability, huge location data can be managed and processed easily by an adversary to obtain user private information from Location-Based Services (LBS). So far, many methods have been proposed to preserve user location privacy for these services. Among them, dummy-based methods have various advantages in terms of implementation and low computation costs. However, they suffer from the spatiotemporal correlation issue when users submit consecutive requests. To solve this problem, a practical hybrid location privacy protection scheme is presented in this paper. The proposed method filters out the correlated fake location data (dummies) before submissions. Therefore, the adversary can not identify the user's real location. Evaluations and experiments show that our proposed filtering technique significantly improves the performance of existing dummy-based methods and enables them to effectively protect the user's location privacy in the environment of big data.

History

Event

IEEE Communication Society. Conference (2017 : Singapore)

Pagination

1 - 6

Publisher

IEEE

Location

Singapore

Place of publication

Piscataway, N.J.

Start date

2017-12-04

End date

2017-12-08

ISBN-13

9781509050192

Language

eng

Publication classification

E Conference publication; E1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

GLOBECOM 2017 : Global hub: connecting east and west : Proceedings of the 2017 IEEE Global Communications Conference