Privacy Preserving Location Data Publishing: A Machine Learning Approach

Type
Article

Authors
Shaham, Sina
Ding, Ming
Liu, Bo
Dang, Shuping
Lin, Zihuai
Li, Jun

KAUST Department
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Online Publication Date
2020-01-07

Print Publication Date
2021-09-01

Date
2020-01-07

Abstract
Publishing datasets plays an essential role in open data research and promoting transparency of government agencies. However, such data publication might reveal users' private information. One of the most sensitive sources of data is spatiotemporal trajectory datasets. Unfortunately, merely removing unique identifiers cannot preserve the privacy of users. Adversaries may know parts of the trajectories or be able to link the published dataset to other sources for the purpose of user identification. Therefore, it is crucial to apply privacy preserving techniques before the publication of spatiotemporal trajectory datasets. In this paper, we propose a robust framework for the anonymization of spatiotemporal trajectory datasets termed as machine learning based anonymization (MLA). By introducing a new formulation of the problem, we are able to apply machine learning algorithms for clustering the trajectories and propose to use k-means algorithm for this purpose. A variation of k-means algorithm is also proposed to preserve the privacy in overly sensitive datasets. Moreover, we improve the alignment process by considering multiple sequence alignment as part of the MLA. The framework and all the proposed algorithms are applied to T-Drive, Geolife, and Gowalla location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

Citation
Shaham, S., Ding, M., Liu, B., Dang, S., Lin, Z., & Li, J. (2020). Privacy Preserving Location Data Publishing: A Machine Learning Approach. IEEE Transactions on Knowledge and Data Engineering, 1–1. doi:10.1109/tkde.2020.2964658

Publisher
Institute of Electrical and Electronics Engineers (IEEE)

Journal
IEEE Transactions on Knowledge and Data Engineering

DOI
10.1109/TKDE.2020.2964658

arXiv
1902.08934

Additional Links
https://ieeexplore.ieee.org/document/8951246/https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8951246http://arxiv.org/pdf/1902.08934

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