Multidimensional Data Integrity Checking Scheme for IoT-Edge Computing-Assisted Intelligent Railway Systems

Abstract

Intelligent railway systems (IRS), based on the end-edge-cloud architecture, can significantly improve train operation safety and the quality of railway services by offering real-time data services. In this scenario, the data integrity protection of IRS data stored in the cloud is critical. Existing data integrity checking (DIC) schemes for user-oriented data storage systems provide proof of data possession by relying on the correctness of randomly sampled data blocks. However, in an IRS-oriented data analysis and storage system, the sampled data blocks are usually generated by massive different data sources. This situation makes it difficult for existing DIC schemes to achieve satisfactory auditing efficiency, rendering them inapplicable to IRS. To handle this challenge, this paper proposes a customized DIC scheme for IoT-edge computing assisted intelligent railway systems (IE-IRS). In particular, a cooperative data authenticator generation mechanism is designed to tackle the challenge of aggregating data authenticators from different sources, thus eliminating the additional communication and computational overhead of repeated verification of response proofs. Aiming at the massive and heterogeneous characteristics of IE-IRS data, a Chinese Remainder Theorem-based data aggregation scheme is developed on the base station side to realize multidimensional data processing. Finally, we formally prove the security of the proposed scheme under the defined security model, and performance analysis demonstrates that it is feasible for IE-IRS.