Biblio

User privacy is an attractive and valuable task to the success of blockchain systems. However, user privacy protection's performance and data capacity have not been well studied in existing access control models of blockchain systems because of traceability and openness of the P2P network. This paper focuses on investigating performance and data capacity from a blockchain infrastructure perspective, which adds secondary encryption to shield confidential information in a non-invasive way. First, we propose an efficient asymmetric encryption scheme by combining homomorphic encryption and state-of-the-art multi-signature key aggregation to preserve privacy. Second, we use smart contracts and CA infrastructure to achieve attribute-based access control. Then, we use the non-interactive zero-knowledge proof scheme to achieve secondary confidentiality explicitly. Finally, experiments show our scheme succeeds better performance in data capacity and system than other schemes. This scheme improves availability and robust scalability, solves the problem of multi-signature key distribution and the unlinkability of transactions. Our scheme has established a sound security cross-chain system and privacy confidentiality mechanism and that has more excellent performance and higher system computing ability than other schemes.

In order to realize Intelligent Disaster Recovery and break the traditional reactive backup mode, it is necessary to forecast the potential system anomalies, and proactively backup the real-time datas and configurations. System logs record the running status as well as the critical events (including errors and warnings), which can help to detect system performance, debug system faults and analyze the causes of anomalies. What's more, with the features of real-time, hierarchies and easy-access, log data can be an ideal source for monitoring system status. To reduce the complexity and improve the robustness and practicability of existing log-based anomaly detection methods, we propose a new anomaly detection mechanism based on hierarchical weights, which can deal with unstable log data. We firstly extract semantic information of log strings, and get the word-level weights by SIF algorithm to embed log strings into vectors, which are then feed into attention-based Long Short-Term Memory(LSTM) deep learning network model. In addition to get sentence-level weight which can be used to explore the interdependence between different log sequences and improve the accuracy, we utilize attention weights to help with building workflow to diagnose the abnormal points in the execution of a specific task. Our experimental results show that the hierarchical weights mechanism can effectively improve accuracy of perdition task and reduce complexity of the model, which provides the feasibility foundation support for Intelligent Disaster Recovery.