A Reconstruction Attack Scheme on Secure Outsourced Spatial Dataset in Vehicular Ad-Hoc Networks

Abstract

In the cloud-based vehicular ad-hoc network (VANET), massive vehicle information is stored on the cloud, and a large amount of data query, calculation, monitoring, and management are carried out at all times. The secure spatial query methods in VANET allow authorized users to convert the original spatial query to encrypted spatial query, which is called query token and will be processed in ciphertext mode by the service provider. Thus, the service provider learns which encrypted records are returned as the result of a query, which is defined as the access pattern. Since only the correct query results that match the query tokens are returned, the service provider can observe which encrypted data are accessed and returned to the client when a query is launched clearly, and it leads to the leakage of data access pattern. In this paper, a reconstruction attack scheme is proposed, which utilizes the access patterns in the secure query processes, and then it reconstructs the index of outsourced spatial data that are collected from the vehicles. The proposed scheme proves the security threats in the VANET. Extensive experiments on real-world datasets demonstrate that our attack scheme can achieve quite a high reconstruction rate.