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.

The low throughput of traditional blockchain can hardly support the demand of high-frequency and high-concurrent application scenarios, which severely restricts the practical application of blockchain and becomes a bottleneck for blockchain development. Sharding is a potential solution to this issue. Existing sharding schemes mostly use complicated PBFT or its variants consensus protocols, with diminishing consensus performance as the number of nodes grows, and global consistency of transaction data is a challenge due to parallel block generation. Aiming at the above problems, we propose an efficient and scalable sharding blockchain system named FortunChain that comprises a verification chain and multiple sharding chains. In the FortunChain, a novel Tyche consensus protocol is designed to select the set of sharding verification nodes and accounting nodes probabilistically leveraging the newly constructed elliptic curve-based verifiable random function (EC-VRF), which ensures the efficiency, unpredictability and security of node selection. Multi-period, fast and dynamic sharding accounting is implemented in the sharding chain to improve the performance of transaction processing. After that, the verification chain is responsible for the secondary verification by applying the batch verification algorithm to the blocks submitted by the shards and then generating the verification block. Each shard stores the sharding state by synchronizing the verification chain to realize the state sharding and ensure the global consistency of the transaction data. Simulation results show that the throughput of FortunChain can reach approximately 6540tx/s and the estimated latency is 8.59s, which demonstrates its efficiency and feasibility.

With the dramatically increase in the scale of Internet data, industrial cloud platforms have put forward higher requirements for data access control and keyword search. Although existing attribute-based encryption technology is suitable for realizing data access control, the problems of large number of matching contents of search results and high computational overhead remain exist. In light of this, an authorized data secure access scheme with specified time and relevance ranked keyword search is proposed to achieve accurate matching of the search results and reduce user’s cost. First, an authorization token on attribute-based access policy and the specified time is constructed. According to the token, the data meeting the access policy is selected within the specified time. Second, we leverage order-preserving index and extended keyword vector to construct keyword index, then the search results of user specified amount are sorted by relevance score. With the use of specified time and relevance for twice screening, the user can get the relevant search results accurately. Finally, the security proof and efficiency analysis show that the scheme is of feasibility.

In the pursuit of automating the entire underground drilling process in coal mines, the automatic rod feeding technology of drilling robots plays a crucial role. However, the current lack of positional accuracy in automatic rod feeding leads to frequent accidents. To address this issue, this paper presents an algorithm for compensating positioning errors in automatic rod feeding. The algorithm is based on a theoretical mathematical model and manual teaching methods. To enhance the positioning accuracy, we first calibrate the pull rope sensor to correct its measurement precision. Subsequently, we establish a theoretical mathematical model for rod feeding positions by employing spatial coordinate system transformations. We determine the target rod feeding position using a manual teaching-based approach. Furthermore, we analyze the relationship between the theoretical rod delivery position and the target rod delivery position and propose an anisotropic spatial difference compensation technique that considers both distance and direction. Finally, we validate the feasibility of our proposed algorithm through automatic rod feeding tests conducted on a coal mine underground drilling robot. The results demonstrate that our algorithm significantly improves the accuracy of rod feeding positions for coal mine underground drilling robots.