MatFBMC

As a typical crowdsourcing service in the healthcare field, medical crowdsourcing allocates tasks to large-scale professionals via edge-cloud collaborative platforms. It provides patients with health consultations and treatment plans. Nevertheless, it is non-trivial to achieve fine-grained access control for medical tasks, fine-grained trusted identification for task requesters, and flexible task selection for medical providers. Furthermore, current access control solutions cannot prevent improper access to medical tasks throughout the full lifecycle, thus making them susceptible to historical and future data breaches. To this end, this paper proposes a privacy-preserving Attribute-Based Matchmaking Encryption scheme called \textit{MatFBMC} that ensures both forward and backward secrecy. It offloads massive puncture operations to the crowdsourcing platform, thereby reducing the client key rotation overhead while ensuring forward secrecy. Meanwhile, this scheme ensures backward secrecy through the embedding of the time epoch, thereby realizing lightweight privilege revocation. Furthermore, the bi-directional matches between access/identification policies and the two parties' attributes are employed to achieve bilateral fine-grained access control. We provide security proofs for semantic security against selective Chosen Plaintext Attack and existential unforgeability under Chosen Message Attack. Based on the Java Pairing-Based Cryptographic Library and real-world medical datasets, simulations display better computational and storage performance compared to the state-of-the-art works.