Preliminary study about advantageous trajectory anonymization methods based on population
Abstract
Growing mobile networks and widely spread of Global Positing System (GPS) devices enables to collect large scale location and trajectory data. Using trajectory data to succeed, privacy and data characteristics are essential. Most anonymization methods are losing characteristic for service providers, like adding noise to trajectories. In this paper, firstly we present adaptive quadtree grid population calculation to determine grid size of trajectories. Our anonymization method dynamically adjust cluster size to maintain trajectory data characteristics, small mesh to the dense populated area, large mesh to sparse populated area, base on quadtree geospatial data structure. Proposed method is satisfied that adaptive size scaling and more efficient to maintain characteristic. Our experiment suggest that proposed method correctly anonymize Tokyo Urban flow data of 1.3M Trajectories.
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By adopting k-anonymity, privacy protection levels can be controlled and quantified.
“Nevertheless, by adopting k-anonymity, the privacy protection levels can be controlled and quantified, which is the main advantage of the anonymity algorithm [28].”
From Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey (2023) · cites this paper as [28]
Cites
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An anonymous communication technique using dummies for location-based services
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Applicability of existing anonymization methods to large location history data in urban travel
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Federated Learning for Privacy Preservation in Smart Healthcare Systems: A Comprehensive Survey
Ali, M., Naeem, F., Tariq, M., & Kaddoum, G. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3181823 · Cited by 366 · see citing works
- Authors
- Hikita, T., & Yamaguchi, R. S.
- Affiliations
- The University of Tokyo (JP)
- Venue
- 2018 10th International Conference on Communication Systems & Networks (COMSNETS)
- Publisher
- IEEE
- Published
- 2018-01
- Pages
- 492-495
- OpenAlex
- W2794554676
- Cited by
- 3
- References
- 9
- Citations by year
- 2022: 1 · 2020: 2
- FWCI
- 0.20
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Communication Systems & Networks (COMSNETS) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Keywords
- trajectory anonymization, location privacy, quadtree grids, urban mobility data, GPS tracks
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