Online/Offline and History Indexing Identity-Based Fuzzy Message Detection

Fuzzy message detection is a novel cryptographic primitive in which the remote storage cloud can help the client carry out fuzzy detections with some false-positive rate. This primitive protects the privacy of clients and does not reveal exactly the matching messages to the untrusted cloud. However, the existing public-key-based schemes require many public keys to generate a single flag ciphertext. This introduces heavy costs in terms of public-key certificate management. In this paper, we propose an efficient identity-based fuzzy message detection method based on a novel identity-based online/offline encryption scheme. All heavy cost computation operations are carried out in the offline phase without the knowledge of the identities for each message, and the single flag ciphertext can be generated quickly with the message's identities since only light cost computations are required in the online phase. We apply our scheme to the blockchain-based fuzzy message detection systems. Additionally, we design a new history indexing scheme based on Dodis' verifiable random function and Schnorr's signature. The transactions history and order of accessing storage cloud (storing and detecting messages) are indexed, and signed by each client. We provide the analysis of privacy guarantees and differential privacy requirements for our online/offline ID-FMD scheme, and implement our FMD system over the Fibos platform and the Huawei Elastic Cloud.

Automated summary

This paper proposes an efficient identity-based fuzzy message detection method that generates a single flag ciphertext quickly through offline and online computations, and applies this method to blockchain technology. Additionally, a history indexing scheme is designed to ensure the order of transaction history and accessing storage cloud, with privacy guarantees and analysis of differential privacy requirements.