The integration of blockchain technology into healthcare presents a paradigm shift for secure data management, enabling decentralized and tamper-proof storage and sharing of sensitive Electronic Health Records (EHRs). However, existing blockchain-based healthcare systems, while providing robust access control, commonly overlook the high latency in user-side re-computation of hashes for integrity verification of large multimedia data, impairing their practicality, especially in time-sensitive clinical scenarios. In this paper, we propose FAITH, an innovative scheme for Fast Authenticated and Interoperable11We focus on the secure data sharing across healthcare institutions. mulTimedia Healthcare data storage and sharing over hybrid-storage blockchains. Rather than user-side hash re-computations, FAITH lets an off-chain storage provider generate verifiable proofs using recursive Zero-Knowledge Proofs (ZKPs), while the user only needs to perform lightweight verification. For flexible access authorization, we leverage Proxy Re-Encryption (PRE) and enable the provider to conduct ciphertext re-encryption, in which the re-encryption correctness can be verified via ZKPs against the malicious provider. All metadata and proofs are recorded on-chain for public verification. We provide a comprehensive analysis of FAITH's security regarding data privacy and integrity. We implemented a prototype of FAITH, and extensive experiments demonstrated its practicality for time-critical healthcare applications, dramatically reducing user-side verification latency by up to 98%, bringing it from 4 s down to around 70 ms for a 5 GB encrypted file.

Secure task matching has been a crucial research problem in crowdsourcing, requiring the alignment of workers’ preferences and requesters’ task requirements while ensuring user privacy and matching integrity. Recently, some researchers applied blockchain to crowdsourcing, either replacing the platform for decentralization or recording proofs for public verification to defend against malicious platforms. However, they still suffer from unitary coarse-grained matching models or expensive on-chain costs. To address these limitations, we propose PGVMatch, a privacy-aware and fine-grained crowdsourcing task-matching scheme with lightweight on-chain public verifiability. Our scheme is constructed on our newly proposed cryptographic primitive–Multi-authority Attribute-Based Keyword Search with Public Verifiability (MABKS-PV), which avoids access policy leakage and key escrow risks on a single authority, meanwhile adding constant-size proof generation and lightweight verification algorithms to a basic ABKS construction. In PGVMatch, requesters can select workers with fine-grained attribute demands, and workers can pick interested tasks with multi-keyword search, preserving dual-side privacy. The matching process is conducted off-chain, while constant-size proofs are recorded on-chain for efficient and public verification of matching integrity. Security analysis and extensive experiments on the Hyperledger Fabric blockchain demonstrate both the security and our superior performance. PGVMatch outperforms the existing scheme with the fastest matching result verification, achieving a 29% improvement in throughput and a 33% reduction in latency.

As a new type of task execution mode, crowdsourcing makes use of crowd/worker intelligence to collaboratively complete diverse tasks published by task requesters. Quality assessment is an important stage in crowdsourcing as the publicly recruited workers often vary in reliability when performing tasks. Prior works on crowdsourcing quality assessment either ignore the possible privacy disclosure from the task data or are vulnerable to biased evaluation from malicious evaluators. In this paper, we propose a privacy-aware verifiable crowdsourcing quality assessment scheme UNITE against semi-honest and malicious adversaries. UNITE explores federated learning for privacy-aware training of task models, which serves as an indicator of quality assessment. To prevent attackers from deducing the task data from model gradients, we design a secure model update protocol based on differential privacy and perform it with blockchain smart contracts for trustworthy model aggregation. In the presence of malicious requesters providing incorrect assessments, we exploit Pedersen Commitment to generate evidence, which is recorded on-chain with some metadata for public audit. Detailed privacy analysis demonstrates that our differential privacy scheme satisfies (ε,δ)-local differential privacy. Finally, we conducted extensive experiments on two real-world datasets and deployed the smart contracts on Hyperledger Fabric to demonstrate good accuracy and both on-chain and off-chain performance.