A hybrid location privacy protection scheme in big data environment
Version 2 2024-06-05, 05:29Version 2 2024-06-05, 05:29
Version 1 2018-04-25, 23:04Version 1 2018-04-25, 23:04
conference contribution
posted on 2024-06-05, 05:29 authored by Mohammad NosouhiMohammad Nosouhi, VVH Pham, S Yu, Yong XiangYong Xiang, M 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.
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Pagination
1-6Location
SingaporePublisher DOI
Start date
2017-12-04End date
2017-12-08ISBN-13
9781509050192Language
engPublication classification
E Conference publication, E1 Full written paper - refereedCopyright notice
2017, IEEETitle of proceedings
GLOBECOM 2017 : Global hub: connecting east and west : Proceedings of the 2017 IEEE Global Communications ConferenceEvent
IEEE Communication Society. Conference (2017 : Singapore)Publisher
IEEEPlace of publication
Piscataway, N.J.Usage metrics
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