Over the years, the flourish of crowd computing has enabled enterprises to accomplish computing tasks through crowdsourcing in a large-scale and high-quality manner, and therefore how to efficiently and securely implement crowd computing becomes a hotspot. Some recent work innovatively adopted a P2P (peer-to-peer) network as the communication environment of crowdsourcing. Based on its decentralized control, issues like single-point-of-failure or DDoS attack can be overcome to some extent, but the huge computing capacity and storage costs required by this scheme is always unbearable. Federated learning is a distributed machine learning that supports local storage of data, and clients implement training through interactive gradient values. In our work, we combine blockchain with federated learning and propose a crowdsourcing framework named CrowdSFL, that users can implement crowdsourcing with less overhead and higher security. In addition, to protect the privacy of participants, we design a new re-encryption algorithm based on Elgamal to ensure that interactive values and other information will not be exposed to other participants outside the workflow. Finally, we have proved through experiments that our framework is superior to some similar work in accuracy, efficiency, and overhead.

The emerging cloud-based IoT paradigm enables IoT devices to directly upload their collected data to the remote cloud and allows data owners to conveniently manage those data through cloud APIs, which has greatly reduced infrastructure investment and data management cost in many IoT applications. Considering that the outsourced data is out of the physical control of data owners and the cloud server cannot always be fully trusted, how to securely delete the unneeded sensitive data stored in cloud to prevent potential data leakage issues is a big challenge. Most of the existing solutions only support coarse-grained deletion and rely on the participation of the cloud server, so their flexibility and practicability are seriously restricted. In this paper, based on an enhanced policy-based puncturable encryption (P-PUN-ENC) primitive, we propose a secure and fine-grained self-controlled outsourced data deletion scheme in cloud-based IoT. The main contribution of our scheme is that it enables data owners to precisely and permanently delete their outsourced IoT-driven data in a policy-based way without relying on the cloud server. To achieve this, we subtly utilize the logical relationship between puncture policy and access policy, and design a policy transform method to convert the puncture process based on puncture policies into update process of access policies. Then, we utilize key delegation technique in attribute-based encryption (ABE) to complete the corresponding key update operations. Additionally, to address the issue of growing key storage and decryption cost in P-PUN-ENC, we propose the outsourced policy-based puncturable encryption (OP-PUN-ENC) primitive by combining the key and decryption outsource technique with P-PUN-ENC. Comprehensive comparisons show that our proposed scheme can better meet the data deletion requirements in cloud-based IoT, and formal security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme.

Cloud computing, which provides a brand-new service model, has become an important infrastructure in the information age, and has been widely used in numerous fields. The Key-Policy Attribute-Based Encryption (KP-ABE) scheme allows the encrypted data with fine-grained access control in the cloud environment. However, achieving large-scale user revocation in the application scenario of KP-ABE becomes one of the thorny problems. Furthermore, the computation and communication costs of the previous user revocation schemes were generally high, especially when a large number of users were revoked. To address these problems, an enhanced high-performance user-revocable KP-ABE scheme combined with the puncture method was proposed. In this article, the user could be revoked by the fine-grained restriction policy. When revoking the user, the cloud would run the puncture algorithm to embed the restriction policy defined by the data owner into the ciphertext. This method could effectively omit the re-encryption and key updating processes, by which the computation and communication overhead of the user revocation are efficiently reduced, and the user revocation becomes more flexible and efficient. Moreover, the Chosen-Plaintext Attack (CPA) security proof and extensive simulation results demonstrate the reliability and efficiency of the proposed scheme for user revocation in a cloud environment.

Cloud-assisted Internet of Medical Things (IoMT) is becoming an emerging paradigm in the healthcare domain, which involves collection, storage and usage of the medical data. Considering the confidentiality and accessibility of the outsourced data, secure and fine-grained data sharing is a crucial requirement for the patients. Attribute-based encryption (ABE) is a promising solution to deal with this issue, but considering its property of each attribute sharing with multiple users, how to flexibly and efficiently update access privileges of certain users without affecting others is still a serious challenge. In this article, we propose a secure and fine-grained data sharing scheme with flexible user access privilege update in cloud-assisted IoMT environment. Specifically, we take ABE as the basic building block, and utilize proxy re-encryption and key blinding techniques to empower the cloud server to re-encrypt the ciphertext affected by revocation and update keys for unrevoked users. In addition, adding attributes for users to extend their access rights is realized only based on few key components stored in cloud without entirely re-computing and re-issuing keys for them. As a result, the patients are able to flexibly and efficiently share their data and manage users’ privileges. Formal proof and detailed performance evaluation demonstrate the security and efficiency of the proposed scheme.

Access control in Internet of Things (IoT) systems has long been facing with single-point-of-failure, collusion and other security issues. Deploying most of the access control procedures on blockchain makes it more trustworthy. However, the miscellaneous transaction data on the public blockchain incurs an extra burden on computing and communication overhead, which hinders its application for IoT devices. Additionally, the demands of rapid response in industrial IoT scenarios (e.g., edge computing scenarios) is hardly realized under a public blockchain. In this paper, we propose an IoT-applicable access control model under an original double-layer blockchain architecture, in which the mortgage/registration and control processes can be disaggregated. Depending on this architecture, the communication overhead for IoT devices is reduced and a rapid response is achieved. With linear secret sharing scheme, attribute-based access policies for resource objects are established to ensure fine-grained access control. We further implement a smart contract prototype on Ethereum and Fisco Bcos. Extensive experiments and numerical analysis demonstrate the feasibility of our proposed model.