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A Data Mining Approach to Assess Privacy Risk in Human Mobility Data

Authors:

Roberto Pellungrini,

Luca Pappalardo,

Francesca Pratesi,

Anna MonrealeAuthors Info & Claims

ACM Transactions on Intelligent Systems and Technology (TIST), Volume 9, Issue 3

Article No.: 31, Pages 1 - 27

https://doi.org/10.1145/3106774

Published: 11 December 2017 Publication History

Abstract

Human mobility data are an important proxy to understand human mobility dynamics, develop analytical services, and design mathematical models for simulation and what-if analysis. Unfortunately mobility data are very sensitive since they may enable the re-identification of individuals in a database. Existing frameworks for privacy risk assessment provide data providers with tools to control and mitigate privacy risks, but they suffer two main shortcomings: (i) they have a high computational complexity; (ii) the privacy risk must be recomputed every time new data records become available and for every selection of individuals, geographic areas, or time windows. In this article, we propose a fast and flexible approach to estimate privacy risk in human mobility data. The idea is to train classifiers to capture the relation between individual mobility patterns and the level of privacy risk of individuals. We show the effectiveness of our approach by an extensive experiment on real-world GPS data in two urban areas and investigate the relations between human mobility patterns and the privacy risk of individuals.

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Cited By

Netzler FLienkamp M(2024)Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph AutoencodersISPRS International Journal of Geo-Information10.3390/ijgi1307024513:7(245)Online publication date: 9-Jul-2024
https://doi.org/10.3390/ijgi13070245
Macha MFoutz NLi BGhose A(2024)Personalized Privacy Preservation in Consumer Mobile TrajectoriesInformation Systems Research10.1287/isre.2023.122735:1(249-271)Online publication date: Mar-2024
https://doi.org/10.1287/isre.2023.1227
Gomes FPellungrini RMonreale ARenso CMartina J(2024)TrajectGuard: A Comprehensive Privacy-Risk Framework for Multiple-Aspects TrajectoriesIEEE Access10.1109/ACCESS.2024.346208812(136354-136378)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462088
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Index Terms

A Data Mining Approach to Assess Privacy Risk in Human Mobility Data
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Multi-task learning
        Transfer learning
    2. Machine learning approaches
      1. Classification and regression trees
2. Security and privacy
  1. Human and societal aspects of security and privacy
    1. Usability in security and privacy
  2. Security services
    1. Pseudonymity, anonymity and untraceability

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cover image ACM Transactions on Intelligent Systems and Technology

ACM Transactions on Intelligent Systems and Technology Volume 9, Issue 3

Regular Papers and Special Issue: Urban Intelligence

May 2018

370 pages

ISSN:2157-6904

EISSN:2157-6912

DOI:10.1145/3167125

Editor:
Yu Zheng
Microsoft Research, China

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Copyright © 2017 ACM.

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Publication History

Published: 11 December 2017

Accepted: 01 May 2017

Revised: 01 May 2017

Received: 01 December 2016

Published in TIST Volume 9, Issue 3

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Cited By

Netzler FLienkamp M(2024)Privacy Preserving Human Mobility Generation Using Grid-Based Data and Graph AutoencodersISPRS International Journal of Geo-Information10.3390/ijgi1307024513:7(245)Online publication date: 9-Jul-2024
https://doi.org/10.3390/ijgi13070245
Macha MFoutz NLi BGhose A(2024)Personalized Privacy Preservation in Consumer Mobile TrajectoriesInformation Systems Research10.1287/isre.2023.122735:1(249-271)Online publication date: Mar-2024
https://doi.org/10.1287/isre.2023.1227
Gomes FPellungrini RMonreale ARenso CMartina J(2024)TrajectGuard: A Comprehensive Privacy-Risk Framework for Multiple-Aspects TrajectoriesIEEE Access10.1109/ACCESS.2024.346208812(136354-136378)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3462088
Terroso-Saenz FSidhu NMuñoz AArcas F(2024)Design of a smart parking space allocation system for higher energy efficiencySmart Spaces10.1016/B978-0-443-13462-3.00016-9(371-390)Online publication date: 2024
https://doi.org/10.1016/B978-0-443-13462-3.00016-9
Jia JLi LQiu PCai BKang XLi XLi X(2024)Domain-Knowledge Enhanced GANs for High-Quality Trajectory GenerationAdvanced Intelligent Computing Technology and Applications10.1007/978-981-97-5606-3_33(386-396)Online publication date: 5-Aug-2024
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Korichi YDesharnais JGambs STawbi N(2024)Leveraging Transformer Architecture for Effective Trajectory-User Linking (TUL) Attack and Its MitigationComputer Security – ESORICS 202410.1007/978-3-031-70903-6_14(271-290)Online publication date: 16-Sep-2024
https://dl.acm.org/doi/10.1007/978-3-031-70903-6_14
Yang HPing AWei HZhai G(2023)Unique in the metro system: The likelihood to re-identify a metro user with limited trajectory pointsPhysica A: Statistical Mechanics and its Applications10.1016/j.physa.2023.129176628(129176)Online publication date: Oct-2023
https://doi.org/10.1016/j.physa.2023.129176
Naretto FPellungrini RRinzivillo SFadda D(2023)EXPHLOT: EXplainable Privacy Assessment for Human LOcation TrajectoriesDiscovery Science10.1007/978-3-031-45275-8_22(325-340)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1007/978-3-031-45275-8_22
Benarous MToch EBen-gal I(2022)Synthesis of Longitudinal Human Location Sequences: Balancing Utility and PrivacyACM Transactions on Knowledge Discovery from Data10.1145/352926016:6(1-27)Online publication date: 30-Jul-2022
https://dl.acm.org/doi/10.1145/3529260
Kou ZShang LZhang YDuan SWang D(2022)Can I only share my eyes? A Web Crowdsourcing based Face Partition Approach Towards Privacy-Aware Face RecognitionProceedings of the ACM Web Conference 202210.1145/3485447.3512256(3611-3622)Online publication date: 25-Apr-2022
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