Work

Protecting sensitive locations in urban movement data through behaviour-preserving map-matched trajectories

Year: 2025

Type: conference-paper

Abstract: This abstract introduces a map-matching-based trajectory anonymisation approach that preserves movement behaviour while protecting sensitive locations with synthetic data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.16923207

Authors: Gatzlaff, Maike, Sila-Nowicka, Katarzyna

Institution: University of Auckland

Language: English

Topic: Privacy-Preserving Technologies in Data

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: green

Funder: Royal Society Te Apārangi

Award: G1205361857

OpenAlex ID: W7079010825

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