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.
Domaines
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