research-article

Movement data anonymity through generalization

Authors:

Gennady Andrienko,

Natalia Andrienko,

Fosca Giannotti,

Dino PedreschiAuthors Info & Claims

SPRINGL '09: Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS

Pages 27 - 31

https://doi.org/10.1145/1667502.1667510

Published: 03 November 2009 Publication History

Abstract

In recent years, spatio-temporal and moving objects databases have gained considerable interest, due to the diffusion of mobile devices (e.g., mobile phones, RFID devices and GPS devices) and of new applications, where the discovery of consumable, concise, and applicable knowledge is the key step. Clearly, in these applications privacy is a concern, since models extracted from this kind of data can reveal the behavior of group of individuals, thus compromising their privacy. Movement data present a new challenge for the privacy-preserving data mining community because of their spatial and temporal characteristics.

In this position paper we briefly present an approach for the generalization of movement data that can be adopted for obtaining k-anonymity in spatio-temporal datasets; specifically, it can be used to realize a framework for publishing of spatio-temporal data while preserving privacy. We ran a preliminary set of experiments on a real-world trajectory dataset, demonstrating that this method of generalization of trajectories preserves the clustering analysis results.

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Cited By

Zhang QZhang XChu ZLi X(2024)Density clustering-based optimization model for trajectory data publicationThe Journal of Supercomputing10.1007/s11227-024-06617-581:1Online publication date: 7-Nov-2024
https://doi.org/10.1007/s11227-024-06617-5
Giotakis SPelekis N(2019)On Preserving Sensitive Information of Multiple Aspect Trajectories In-HouseCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317594(515-522)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317594
He JChen HChen YTang XZou Y(2019)Variable-Based Spatiotemporal Trajectory Data Visualization IllustratedIEEE Access10.1109/ACCESS.2019.29428447(143646-143672)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2942844
Show More Cited By

Index Terms

Movement data anonymity through generalization
1. Information systems
  1. Information systems applications
2. Social and professional topics
  1. Computing / technology policy

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Published In

cover image ACM Conferences

SPRINGL '09: Proceedings of the 2nd SIGSPATIAL ACM GIS 2009 International Workshop on Security and Privacy in GIS and LBS

November 2009

79 pages

ISBN:9781605588537

DOI:10.1145/1667502

General Chair:
Elisa Bertino
Purdue University
,
Program Chairs:
Maria Luisa Damiani
University of Milan, Italy
,
Yücel Saygin
Sabanci University, Turkey

Copyright © 2009 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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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 November 2009

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GIS '09

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SIGSPATIAL

GIS '09: 17th SIGSPATIAL International Conference on Advances in Geographic Information Systems

November 3, 2009

Washington, Seattle

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Citations

Cited By

Zhang QZhang XChu ZLi X(2024)Density clustering-based optimization model for trajectory data publicationThe Journal of Supercomputing10.1007/s11227-024-06617-581:1Online publication date: 7-Nov-2024
https://doi.org/10.1007/s11227-024-06617-5
Giotakis SPelekis N(2019)On Preserving Sensitive Information of Multiple Aspect Trajectories In-HouseCompanion Proceedings of The 2019 World Wide Web Conference10.1145/3308560.3317594(515-522)Online publication date: 13-May-2019
https://dl.acm.org/doi/10.1145/3308560.3317594
He JChen HChen YTang XZou Y(2019)Variable-Based Spatiotemporal Trajectory Data Visualization IllustratedIEEE Access10.1109/ACCESS.2019.29428447(143646-143672)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2942844
Wang SChen CZhang GXin Y(2019)Interchange-Based Privacy Protection for Publishing TrajectoriesIEEE Access10.1109/ACCESS.2019.29427207(138299-138314)Online publication date: 2019
https://doi.org/10.1109/ACCESS.2019.2942720
Tu ZLi RLi YWang GWu DHui PSu LJin D(2018)Your Apps Give You AwayProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/32649482:3(1-23)Online publication date: 18-Sep-2018
https://dl.acm.org/doi/10.1145/3264948
Song JDuan BChou HTsai H(2018)On Cloaking Sensitive Pattern Sets for Long-Term LBS Applications2018 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)10.1109/ICCE-China.2018.8448574(1-2)Online publication date: May-2018
https://doi.org/10.1109/ICCE-China.2018.8448574
Wang HXu ZJia S(2017)Cluster-Indistinguishability: A practical differential privacy mechanism for trajectory clusteringIntelligent Data Analysis10.3233/IDA-16309821:6(1305-1326)Online publication date: 15-Nov-2017
https://doi.org/10.3233/IDA-163098
Xu FTu ZLi YZhang PFu XJin DBarrett RCummings RAgichtein EGabrilovich E(2017)Trajectory Recovery From AshProceedings of the 26th International Conference on World Wide Web10.1145/3038912.3052620(1241-1250)Online publication date: 3-Apr-2017
https://dl.acm.org/doi/10.1145/3038912.3052620
CAO YYOSHIKAWA M(2016)Differentially Private Real-Time Data Publishing over Infinite Trajectory StreamsIEICE Transactions on Information and Systems10.1587/transinf.2015EDP7096E99.D:1(163-175)Online publication date: 2016
https://doi.org/10.1587/transinf.2015EDP7096
Tai FSong JTsai YTsai H(2016)Cloaking sensitive patterns Td preserve location privacy for LBS applications2016 IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)10.1109/ICCE-TW.2016.7520964(1-2)Online publication date: May-2016
https://doi.org/10.1109/ICCE-TW.2016.7520964
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