Cited by 52Privacy-Preserving Technologies in DataData Management and AlgorithmsHuman Mobility and Location-Based Analysis

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SafePath: Differentially-private publishing of passenger trajectories in transportation systems

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Crossref · 10.1016/j.comnet.2018.07.007

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Research works printed but not linked: 20

  1. Abraham, 2012, Spatio-temporal similarity of network-constrained moving object trajectories using sequence alignment of travel locations, Transp. Res. Part C, 23, 109, 10.1016/j.trc.2011.12.008 · DOI
  2. Burger, 2013, Considerations for model-based traffic control, Transp. Res. Part C, 35, 1, 10.1016/j.trc.2013.05.011 · DOI
  3. Chen, 2013, Privacy-preserving trajectory data publishing by local suppression, Inf. Sci., 231, 83, 10.1016/j.ins.2011.07.035 · DOI
  4. Cicek, 2014, Ensuring location diversity in privacy-preserving spatio-temporal data publishing, VLDB J., 23, 609, 10.1007/s00778-013-0342-x · DOI
  5. Coifman, 1998, A real-time computer vision system for vehicle tracking and traffic surveillance, Transp. Res. Part C, 6, 271, 10.1016/S0968-090X(98)00019-9 · DOI
  6. Dwork, 2011, A firm foundation for private data analysis, Commun. ACM, 54, 86, 10.1145/1866739.1866758 · DOI
  7. ElSalamouny, 2016, Differential privacy models for location-based services, Trans. Data Priv., 9, 15
  8. Ghasemzadeh, 2014, Anonymizing trajectory data for passenger flow analysis, Transp. Res. Part C, 39, 63, 10.1016/j.trc.2013.12.003 · DOI
  9. Hay, 2010, Boosting the accuracy of differentially private histograms through consistency, Proc. VLDB Endow., 3, 1021, 10.14778/1920841.1920970 · DOI
  10. He, 2015, DPT: differentially private trajectory synthesis using hierarchical reference systems, Proc. VLDB Endow., 8, 1154, 10.14778/2809974.2809978 · DOI
  11. Ho, 2013, Preserving privacy for interesting location pattern mining from trajectory data, Trans. Data Priv., 6, 87
  12. Hu, 2010, Privacy-aware location data publishing, ACM Trans. Datab. Syst., 35, 18:1
  13. Monreale, 2010, Movement data anonymity through generalization, Trans. Data Priv., 3, 91
  14. Qardaji, 2013, Understanding hierarchical methods for differentially private histograms, Proc. VLDB Endow., 6, 1954, 10.14778/2556549.2556576 · DOI
  15. Sherkat, 2013, Efficient time-stamped event sequence anonymization, ACM Trans. Web, 8, 4:1, 10.1145/2532643 · DOI
  16. Sweeney, 2002, K-anonymity: a model for protecting privacy, Int. J. Uncertain. Fuzziness Knowl. Syst., 10, 557, 10.1142/S0218488502001648 · DOI · Paper page
  17. Tang, 2014, A framework of traveling companion discovery on trajectory data streams, ACM Trans. Intell. Syst. Technol., 5, 3:1
  18. Wang, 2016, A review of differential privacy in individual data release, Int. J. Distrib. Sens. Netw., 2015, 1:1
  19. Xiao, 2011, Differential privacy via wavelet transforms, IEEE Trans. Knowl. Data Eng., 23, 1200, 10.1109/TKDE.2010.247 · DOI
  20. Zekri, 2014, Building, sharing and exploiting spatio- temporal aggregates in vehicular networks, Mob. Inf. Syst., 10, 259

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Metadata

Authors
Al-Hussaeni, K., Fung, B. C. M., Iqbal, F., Dagher, G. G., & Park, E. G.
Affiliations
  • Concordia University (CA)
  • McGill University (CA)
  • Zayed University (AE)
  • Boise State University (US)
Corresponding author
Fung, Benjamin C.M.
Venue
Computer Networks
Publisher
Elsevier BV
Published
2018-10
Volume
143
Pages
126-139
ISSN
1389-1286 (ISSN-L)
OpenAlex
W2832811676
Cited by
52
References
1
Citations by year
2026: 3 · 2025: 8 · 2024: 8 · 2023: 4 · 2022: 10 · 2021: 8 · 2020: 5 · 2019: 3 · 2018: 3
FWCI
2.59
Type
journal article
Language
en
Open access
Locations
  • Computer Networks (publishedVersion)
  • Spectrum Research Repository (Concordia University) (acceptedVersion)
  • ScholarWorks (Boise State University) (submittedVersion)
  • eScholarship@McGill (McGill) (submittedVersion)
  • eScholarship@McGill (McGill) (submittedVersion, cc-by-nc-nd)
Funders
  • Natural Sciences and Engineering Research Council of Canada (RGPIN-2018-03872)
  • Canada Research Chairs Program (950-230623)
  • Zayed University (R16083)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
  • Physical Sciences › Computer Science › Information Systems › Data Management and Algorithms
  • Social Sciences › Transportation › Human Mobility and Location-Based Analysis
Keywords
differential privacy, spatio-temporal data, privacy-preserving data publishing, trajectory anonymization, transit data, human mobility data, data utility, data sparsity

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