research-article

A Distributed Approach for Privacy Preservation in the Publication of Trajectory Data

Authors:

Felipe T. Brito,

Antônio C. Araújo Neto,

Camila F. Costa,

André L. C. Mendonça,

Javam C. MachadoAuthors Info & Claims

GeoPrivacy'15: Proceedings of the 2nd Workshop on Privacy in Geographic Information Collection and Analysis

Article No.: 5, Pages 1 - 8

https://doi.org/10.1145/2830834.2830835

Published: 03 November 2015 Publication History

Abstract

Advancements in mobile computing techniques along with the pervasiveness of location-based services have generated a great amount of trajectory data. These data can be used for various data analysis purposes such as traffic flow analysis, infrastructure planning and understanding of human behavior. However, publishing this amount of trajectory data may lead to serious risks of privacy breach. Quasi-identifiers are trajectory points that can be linked to external information and be used to identify individuals associated with trajectories. Therefore, by analyzing quasi-identifiers, one may be able to trace anonymous trajectories back to individuals with the aid of location-aware social networking applications, for example. Most existing trajectory data anonymization approaches were proposed for centralized computing environments, so they usually present poor performance to anonymize large trajectory data sets. In this paper we propose a distributed and efficient strategy that adopts the km-anonymity privacy model and uses the scalable MapReduce paradigm, which allows finding quasi-identifiers in larger amount of data. We also present a technique to minimize the loss of information by selecting key locations from the quasi-identifiers to be suppressed. Experimental evaluation results demonstrate that our proposed approach for trajectory data anonymization is more scalable and efficient than existing works.

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Cited By

Yao LChen ZHu HWu GWu B(2022)Privacy Preservation for Trajectory Publication Based on Differential PrivacyACM Transactions on Intelligent Systems and Technology10.1145/347483913:3(1-21)Online publication date: 12-Apr-2022
https://dl.acm.org/doi/10.1145/3474839
Shaham SDing MLiu BDang SLin ZLi J(2021)Privacy Preserving Location Data Publishing: A Machine Learning ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.296465833:9(3270-3283)Online publication date: 1-Sep-2021
https://doi.org/10.1109/TKDE.2020.2964658
Yao LChen ZHu HWu GWu B(2020)Sensitive attribute privacy preservation of trajectory data publishing based on l-diversityDistributed and Parallel Databases10.1007/s10619-020-07318-7Online publication date: 17-Nov-2020
https://doi.org/10.1007/s10619-020-07318-7
Show More Cited By

Index Terms

A Distributed Approach for Privacy Preservation in the Publication of Trajectory Data

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Published In

cover image ACM Conferences

GeoPrivacy'15: Proceedings of the 2nd Workshop on Privacy in Geographic Information Collection and Analysis

November 2015

40 pages

ISBN:9781450339698

DOI:10.1145/2830834

Editors:
Grant McKenzie
University of California, Santa Barbara, USA
,
Krzysztof Janowicz
University of California, Santa Barbara, USA
,
Gueorgi Kossinets
Google Inc., Mountain View, USA

Copyright © 2015 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGSPATIAL: ACM Special Interest Group on Spatial Information

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SIGGRAPH: ACM Special Interest Group on Computer Graphics and Interactive Techniques

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2015

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SIGSPATIAL'15: 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3 - 6, 2015

WA, Bellevue, USA

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Overall Acceptance Rate 5 of 8 submissions, 63%

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Cited By

Yao LChen ZHu HWu GWu B(2022)Privacy Preservation for Trajectory Publication Based on Differential PrivacyACM Transactions on Intelligent Systems and Technology10.1145/347483913:3(1-21)Online publication date: 12-Apr-2022
https://dl.acm.org/doi/10.1145/3474839
Shaham SDing MLiu BDang SLin ZLi J(2021)Privacy Preserving Location Data Publishing: A Machine Learning ApproachIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2020.296465833:9(3270-3283)Online publication date: 1-Sep-2021
https://doi.org/10.1109/TKDE.2020.2964658
Yao LChen ZHu HWu GWu B(2020)Sensitive attribute privacy preservation of trajectory data publishing based on l-diversityDistributed and Parallel Databases10.1007/s10619-020-07318-7Online publication date: 17-Nov-2020
https://doi.org/10.1007/s10619-020-07318-7
Yao LWang XWang XHu HWu G(2019)Publishing Sensitive Trajectory Data Under Enhanced l-Diversity Model2019 20th IEEE International Conference on Mobile Data Management (MDM)10.1109/MDM.2019.00-61(160-169)Online publication date: Jun-2019
https://doi.org/10.1109/MDM.2019.00-61
Yao LWang XChen ZWu G(2019)Privacy Preservation in Publishing Electronic Health Records Based on PerturbationSecurity and Privacy in New Computing Environments10.1007/978-3-030-21373-2_12(125-140)Online publication date: 8-Jun-2019
https://doi.org/10.1007/978-3-030-21373-2_12
Yang NLiu LQian ZLi Q(2018)Trajectory Privacy Preserving for LBS in P2P EnvironmentProceedings of the 3rd International Conference on Big Data and Computing10.1145/3220199.3220209(134-138)Online publication date: 28-Apr-2018
https://dl.acm.org/doi/10.1145/3220199.3220209
Zhang HWu CChen ZLiu ZZhu Y(2017)A novel on-line spatial-temporal k-anonymity method for location privacy protection from sequence rules-based inference attacksPLOS ONE10.1371/journal.pone.018223212:8(e0182232)Online publication date: 2-Aug-2017
https://doi.org/10.1371/journal.pone.0182232

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