Privacy Preserving Location Data Publishing: A Machine Learning Approach

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 TDrive and Geolife location datasets. The experimental results indicate a significantly higher utility of datasets by anonymization based on MLA framework.

A Semantically Sensitive Privacy Protection Method for Trajectory Publishing

“…More recently, Shaham et al [10] conducted a research about how to protect the trajectory data with machine learning techniques. They designed a new privacy protection framework called MLA, which accepts the original data set and a parameter of the privacy metric k. The framework is consists of three procedures: generalization, alignment and clustering, and the output will be an anonymized data set.…”

“…Because of such privacy leakage, the victim might suffer from annoying advertising and fraud, or even encounter life safety threats in extreme cases. This issue has received much attention from the academia, and researchers have proposed various approaches to protect the trajectory data, including dummy-based approach [5] [6], trajectory synthesis for k-anonymity [7] [8], suppression approach [9] and machine learning approach [10].…”

A Comprehensive Survey of Enabling and Emerging Technologies for Social Distancing—Part II: Emerging Technologies and Open Issues

“…Specifically, we can anonymize/randomize/obfuscate/perturb the exact location of each mobile user to avoid malicious attacks from the attackers using the following mechanisms. For example, the authors in [127] develop a privacypreserving location-based framework to anonymize spatiotemporal trajectory datasets utilizing machine-learning-based anonymization (MLA). In this case, the framework applies the K-means machine learning algorithm to cluster the trajectories from real-world GPS datasets and ensure the K-anonymity for high-sensitive datasets.…”

“…Specifically, we can anonymize/randomize/obfuscate/perturb the exact location of each mobile user to avoid malicious attacks from the attackers using the following mechanisms. For example, the authors in [126] develop a privacy-preserving location-based framework to anonymize spatio-temporal trajectory datasets utilizing machine-learning-based anonymization (MLA). In this case, the framework applies the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -means machine learning algorithm to cluster the trajectories from real-world GPS datasets and ensure the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$K$ \end{document} -anonymity for high-sensitive datasets.…”

Privacy and Utility Preserving Trajectory Data Publishing for Intelligent Transportation Systems

“…Furthermore, the WCOP-SA algorithm in [24] partitions trajectories into several segments during the anonymization process aiming at improving data utility. In [25], machine learning algorithms are applied to cluster the trajectories and a variation of the k-means algorithm is developed to preserve the privacy in overly sensitive datasets.…”