KEYWORDS: Data modeling, Computer security, Control systems, Network security, Data transmission, Data processing, Data storage, Data fusion, Systems modeling, Lithium
In this paper, an extended access control mechanism is proposed for controlled sharing of data after data flow in complex network environment, which provides more secure, efficient and personalized data access methods, ensuring that users can flexibly obtain data that meet their requirements. The proposed control mechanism is divided into two categories: constraint control and propagation control. Among them, constraint control solves the problem of access authorization of data before access request by the access request entity, and propagation control is used for extended control of data after data leave the data center. The proposed mechanism realizes direct and indirect access control of data, and takes the whole life control of electronic invoices as an example to show the implementation method of the proposed mechanism.
KEYWORDS: Internet of things, Java, Standards development, Field programmable gate arrays, Systems modeling, Information security, Computer hardware, Software development, Lithium, Computer security
Elliptic Curve Cryptography(ECC) is widespread in practical applications because compared to other asymmetric cryptosystems, ECC can achieve the same level of security with shorter key lengths. With the development of the Internet of Things(IoT), a great deal of constrained devices may require software ECC implementations. However, point multiplication, which is the most time-consuming operation, restricts the use of ECC in these devices. In order to improve the efficiency of the SM2 algorithm based on ECC in IoT, we propose an efficient and secure distributed scheme for SM2 decryption. In this proposed scheme, the SM2 private key is covertly divided into key shares of different lengths, which are then distributed to various devices to ensure the privacy of the key. Additionally, high-performance devices handle auxiliary point multiplication calculations, improving the decryption speed and alleviating the burden of local calculations. Performance analysis and experiments demonstrate that the scheme achieves nearly 3.8 times faster decryption than the standard SM2 algorithm, making it suitable for IoT applications.
This paper proposes an intelligent discrepancy analysis method based on natural language processing to address the issue of standard discrepancy in the electricity industry. The objective of this method is to identify discrepancies between standards and pinpoint their distinguishing factors, in order to achieve better standardization, regulation, and consistency. This will enhance the safety and reliability of electricity equipment and systems, while reducing production, operation, and management costs. The paper first builds an electricity standard discrepancy dataset using the open-world assumption theory. Then, it uses a noisy method to fine-tune the SBERT model for identifying discrepancies in electricity standard clauses. Finally, by optimizing the SimCSE model with relaxed optimal transport distance, the interpretability of the model is improved and a text similarity matrix is obtained, enabling the visualization of discrepancies in clause text. The precision and recall rates of standard discrepancy identification achieved by this method are 81.54% and 82.78%, respectively. This method not only helps to improve the sustainable development of the electricity industry, but also provides more data support and decision-making references for electricity enterprises to better address issues related to standard management and implementation.
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