A Lightweight Approach for Origin-Destination Matrix Anonymization

Abstract

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/Destinationmatrix 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.