https://doi.org/10.1140/epjds/s13688-025-00611-4
Research
Impact of federated data with local differential privacy for human mobility modeling
1
Network Science Institute, Northeastern University, Boston, USA
2
Department of Computer Science, University College London, London, UK
3
Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
4
Department of Geography, University College London, London, UK
5
Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK
a
This email address is being protected from spambots. You need JavaScript enabled to view it.
b
This email address is being protected from spambots. You need JavaScript enabled to view it.
Received:
18
January
2025
Accepted:
12
December
2025
Published online:
16
January
2026
Abstract
With increasing awareness of the privacy risks posed by mobile phone location data, researchers need ways to use mobility data while offering stronger privacy guarantees to the individuals included in this data. A promising approach to this challenge is the creation of privacy-preserving mobility insights from decentralized location data using Local Differential Privacy (LDP). However, mobility data generated with LDP, based on the introduction of noise by individual mobile devices, is limited by the volume of noise required to achieve individual privacy. In this paper, we provide a fully reproducible model of the accuracy of mobility networks generated with LDP compared to mobility network data generated with more traditional privacy mechanisms: Central Differential Privacy (CDP) and K-anonymity. Using a simulated mobile phone mobility dataset informed by real-world travel patterns in the USA, we explore the trade-off between privacy and data utility provided by different parameters in a federated system with LDP. We also explore the impact of spatial and temporal aggregation on data accuracy, showing that long-standing considerations regarding the appropriate units of analysis for geographic data play a key role in determining the utility of federated mobility data with LDP. Our paper facilitates an in-depth understanding of the trade-offs between privacy and data utility entailed by the future adoption of a federated approach which uses LDP to generate insights from decentralized mobility data.
Key words: Human mobility / Differential privacy / Local differential privacy / Ethics / Federated analytics / GPS / Call detail records
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjds/s13688-025-00611-4.
Handling Editor: Rossano Schifanella
© The Author(s) 2026
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
