Federated learning is an emerging technology that solves the privacy problem of training data in multi-party machine learning. However, this technology is vulnerable to a series of system security problems. In this letter, we leverage Hyperledger Fabric permissioned blockchain architecture to build a secure and reliable federated learning platform across multiple data owners, where individual local updates are encrypted based on threshold homomorphic encryption and then recorded on a distributed ledger. The security analysis shows that our solution can effectively deal with the existing privacy and security issues in the federated learning system. The numerical results show that the scheme is feasible and efficient.

Medical data sharing is of great significance in promoting smart medicine. However, the heterogeneity of information systems used by various medical institutions makes sharing difficult. In addition, since medical data involves a great deal of sensitive information, sharing it could easily lead to the leakage of personal privacy. Blockchain, gained popularity as a distributed ledger technology, has great potential to connect heterogeneous systems and provides authenticity and integrity guarantees for medical data sharing. Focusing on the issues of medical data sharing and privacy protection, we propose a medical data sharing scheme based on consortium blockchain. To achieve access control, attribute-based access control technique is implemented, where patients preset attribute-specific access policies for their medical records, and record requesters are described by a set of attributes. For patients, we devise a hybrid storage mode to write access policies of medical records on the consortium blockchain network and store encrypted medical records off-chain. Leveraging blockchain and smart contracts, access privilege control and access history tracking can be realized. To enhance the key management, a tree of medical records is constructed for each patient, and by simply keeping the medical record trees, patients can recover their encryption keys at any time. Furthermore, we carry out an extensive analysis to show the high security and efficiency of our proposed scheme. Finally, we build a Quorum consortium blockchain on the Tencent Cloud and deploy smart contracts on the chain to simulate transactions in our scheme. The experiment results indicate the proposed scheme achieves good feasibility.

The insurance business plays a quite significant role in people’s lives, but in the process of claim settlement, there are still various frauds such that the insurance companies’ refusal to compensate or customers’ malicious fraud to obtain compensation. Therefore, it is very important to ensure fair and just claims. In this paper, by combining the blockchain technology and the ciphertext-policy attributebased encryption system, we build a scheme for secure storage and update for insurance records under the InterPlanetary File System (IPFS) storage environment in the insurance system. In this scheme, we use the fog node to outsource encryption of insurance records to improve the efficiency of the staff; In addition, we store encrypted insurance records on IPFS to ensure the security of the storage platform and avoid the single point failure of the centralized mechanism. In addition, we use the immutability of the blockchain to achieve the non-repudiation of both insurance companies and the client. The security proof shows that the proposed scheme can achieve selective security against selected keyword attacks. Our scheme is efficient and feasible under performance analysis and real data set experiments.

In a ciphertext-policy attribute-based encryption (CP-ABE) scheme, a user may have multiple attributes, and each attribute may be shared simultaneously by many users. The decryption key of an attribute can thus be shared by many users who all possess the attribute. For monetary gain, a malicious authorized user may reveal his/her decryption key to a third party, and it is difficult to trace the owner of primitive secret key from an exposed key. At the same time, this situation may also limit commercial applications of CP-ABE systems. To solve these problems and enable fine-grained access control for the encrypted data, we propose a traceable CP-ABE scheme with attribute-level user revocation for cloud storage (TUR-CPABE). Our scheme enjoys four advantages. First, it has the ability to trace malicious users who have leaked key information from the system. Second, it supports attribute-level user revocation for malicious users and allows ABE fine-grained access control. Third, it allows secret key updates and ciphertext updates to resist collusion attacks between users. Fourth, outsourcing encryption, decryption and attribute revocation are used to reduce the computational burden on data owners, data users and the trust authority, respectively. In addition, our scheme has been proven to be secure against chosen plaintext attacks under a selective access policy based on decisional q – BDHE assumption in the standard model.