Privacy-preserving and efficient data sharing for blockchain-based intelligent transportation systems

Abstract

Recent years have witnessed the development and adoption of blockchain technology in intelligent transportation systems (ITS) because of its authenticity and traceability. However, increasing ITS devices impose grand challenges in privacy-preserving and efficient data sharing. Recent research has demonstrated that integrating searchable symmetric encryption in blockchain enables privacy-preserving data sharing among ITS devices. However, existing solutions focus only on single-keyword searches over encrypted ITS data on the blockchain and suffer from privacy and efficiency issues when extended to multi-keyword scenarios. This work proposes a bloom filter-based multi-keyword search protocol for ITS data with enhanced efficiency and privacy preservation. We design a bloom filter to select a low-frequency keyword from the multiple keywords input by the ITS data owner. The low-frequency keyword can filter out a large portion of the ITS data from the search result, thus significantly reducing the computational cost. Furthermore, each identifier-keyword pair is attached with a pseudorandom tag that enables the completion of a search operation in only one round. In this manner, privacy is preserved because there are no intermediate rounds and results. In addition to the multi-keyword search protocol, we specify the addition and deletion protocols to enable dynamic updates of data records. We conducted a comprehensive performance evaluation of the protocols. The experimental results indicate that the proposed multi-keyword search protocol saves 14.67% query time and 59.96% financial cost.