Towards fully verifiable forward secure privacy preserving keyword search for IoT outsourced data

Abstract

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for cloud-based IoT data. Forward security makes privacy preserving keyword search schemes for cloud-based IoT data able to resist file injection attack, which is one new attack on privacy preserving keyword search when IoT data is updated. Verifiability makes privacy preserving keyword search schemes for cloud-based IoT data able to ensure the validity of the search results returned by cloud. However, to the best of our knowledge, all of existing privacy preserving keyword search schemes at most achieve forward security and partial verifiability. In order to solve this problem, we explore how to simultaneously achieve the forward security and the full verification for intelligent encrypted data processing in cloud-based IoT and propose the first fully verifiable forward secure privacy preserving keyword search scheme for IoT outsourced data. In order to achieve forward security, we construct the secure index based on the hash chain. In each update, the IoT data owner randomly chooses a string as a new state to generate the new trapdoor. The new trapdoor cannot match previous trapdoors. In order to achieve the full verifiability while still maintaining forward security for intelligent encrypted data processing in cloud-based IoT, we design novel authentication construction called Forward Secure Accumulative Authentication Tag (FSAAT). This kind of tag has the incremental property and supports the verification on the basis of efficient data update. We prove that our scheme is secure and efficient through detailed security analysis and experiments.