Abstract
Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching.
This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints.
DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model
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- Developers:
-
Peisert, Sean [1] ; Macfarlane, Jane [1][2][3] ; Zhang, Michael [4] ; Chuah, Chen-Nee [4] ; Haydari, Ammar [4]
- Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- University of California, Berkeley (UCB)
- Seurat Labs
- University of California, Davis (UCD)
- Release Date:
- 2025-03-05
- Project Type:
- Open Source, Publicly Available Repository
- Software Type:
- Scientific
- Licenses:
-
BSD 3-clause "New" or "Revised" License
- Sponsoring Org.:
-
USDOEPrimary Award/Contract Number:AC02-05CH11231Other Award/Contract Number:The Regents of the University of California No. 830404000
- Code ID:
- 166078
- Site Accession Number:
- 2025-002
- Research Org.:
- Seurat LabsUniversity of California, Davis (UCD)Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
- Country of Origin:
- United States
Citation Formats
Peisert, Sean, Macfarlane, Jane, Zhang, Michael, Chuah, Chen-Nee, and Haydari, Ammar.
Differentially Private Map Matching (DPMM) v1.0.
Computer Software.
https://github.com/lbnl-cybersecurity/DPMM.
USDOE.
05 Mar. 2025.
Web.
doi:10.11578/dc.20251002.10.
Peisert, Sean, Macfarlane, Jane, Zhang, Michael, Chuah, Chen-Nee, & Haydari, Ammar.
(2025, March 05).
Differentially Private Map Matching (DPMM) v1.0.
[Computer software].
https://github.com/lbnl-cybersecurity/DPMM.
https://doi.org/10.11578/dc.20251002.10.
Peisert, Sean, Macfarlane, Jane, Zhang, Michael, Chuah, Chen-Nee, and Haydari, Ammar.
"Differentially Private Map Matching (DPMM) v1.0." Computer software.
March 05, 2025.
https://github.com/lbnl-cybersecurity/DPMM.
https://doi.org/10.11578/dc.20251002.10.
@misc{
doecode_166078,
title = {Differentially Private Map Matching (DPMM) v1.0},
author = {Peisert, Sean and Macfarlane, Jane and Zhang, Michael and Chuah, Chen-Nee and Haydari, Ammar},
abstractNote = {Human mobility trajectories provide valuable information for developing mobility applications, as they contain diverse and rich information about the users. User mobility data is valuable for various applications such as intelligent transportation systems (ITS), commercial business models, and disease-spread models. However, such spatio-temporal traces may pose a threat to user privacy. GPS trajectories in their raw form are not suitable for transportation studies, as they require matching locations with nearest road links — a process called map-matching.
This software implements a differential privacy (DP)-based map-matching algorithm, called DPMM, that generates link-level location trajectories in a privacy-preserving manner to protect users' origin destinations (OD) and travel paths. OD privacy is achieved by injecting Planar Laplace noise to the user OD GPS points. Travel-path privacy is provided with randomized travel path construction using exponential DP mechanism. The injected noise level is selected adaptively, by considering the link density of the location and the functional category of the localized links. For path privacy, our mechanism samples waypoints and selects candidate paths between waypoints.
DPMM provides privacy effectively with respect to link density instead of other trajectory samples in the database compared to other privacy mechanisms. Compared to the different baseline models our DP-based privacy model offers closer query responses to the raw data in terms of individual and aggregate trajectory-level statistics with an average at absolute deviation from the baseline for individual statistics on ϵ = 1.0. Beyond individual trajectory statistics, the DPMM outperforms the other benchmark DP-based mechanisms on different aggregate statistics with up to 8x improvement in utility.},
doi = {10.11578/dc.20251002.10},
url = {https://doi.org/10.11578/dc.20251002.10},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20251002.10}},
year = {2025},
month = {mar}
}