Anonymity-based data publishing for preserving customer privacy in railway systems

In the big data era, data analysis has attracted more and more attentions in many industry areas such as intelligent transportation. The customer information in railway systems is quite valuable for marketing purposes, as it can reflect a user's travel pattern. Therefore, the privacy issues have been brought out. Recently, many studies focus on access control and other traditional security problems in transportation systems, and little studied on the topic of the private data publishing. In this paper, we study the private customer data publishing problem by representing the data with a hypergraph, which is quite efficient to illustrate complex relationships among customers. We provide an anonymity-based approach to hide the identities of customers to protect their sensitive information. We also take data utility into consideration by defining specific information loss metrics. The performances of the methods have been validated by extensive experiments.