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Communication Dans Un Congrès Année : 2021

A lightweight approach for origin-destination matrix anonymization

Résumé

Personal trajectory data are becoming more and more accessible and have a high value in transport planning and mobility characterisation, at the cost of a risk for user's privacy. Addressing this risk is usually computationally expensive and can lead to losing most of the data utility. We explore a new, light-weight approach to Origin/Destination-matrix anonymization that is easily scalable. We apply it to trip records from New York City Taxi and Limousine Commission (TLC) to illustrate how it can combine foolproof anonymity with a good spatial precision for a reasonable computational cost.

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Autre
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Dates et versions

hal-03922211 , version 1 (11-09-2023)

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Citer

Benoît Matet, Etienne Come, Furno Angelo, Loïc Bonnetain, Latifa Oukhellou, et al.. A lightweight approach for origin-destination matrix anonymization. ESANN 2021, The 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Oct 2021, Bruges, Belgium. pp 487-492, ⟨10.14428/esann/2021.ES2021-56⟩. ⟨hal-03922211⟩
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