Vector Sum Range Decision for Verifiable Multiuser Fuzzy Keyword Search in Cloud-Assisted IoT

To better leverage the massive data obtained by Internet of Things (IoT) devices, an increasing number of IoT applications are choosing to outsource data to the cloud. However, outsourcing services may cause the issue of privacy leakage. Fuzzy keyword searchable encryption is a desirable tool to provide flexible searching functionality while maintaining the privacy of data. Such schemes consist of two essential components: the underlying match algorithm, such as the fuzzy dictionary, locality-sensitive hashing, and bloom filter, and the privacy preserving mechanism, including symmetric encryption, asymmetric encryption, etc. However, most fuzzy keyword search schemes hardly balance accuracy, efficiency, and multiuser simultaneously. We construct a verifiable multiuser fuzzy search system for cloud-assisted IoT scenarios. Our contribution is twofold. First, we propose a novel vector sum range decision (VSRD) fuzzy search technique with high accuracy, which is of independent interest. Second, to ensure privacy and efficiency, we combine VSRD and Shamir’s threshold scheme to design a verifiable multiuser fuzzy keyword search (MFKS) distributed system, which supports dynamic update. Extensive analyses and experiments demonstrate that our MFKS system resists inside keyword guessing attacks and enjoys high accuracy and efficiency.