Blockchain faces scalability issues. Sharding improves system performance by dividing the blockchain network into multiple subnetworks that process transactions in parallel. However, sharding can lead to the clustering of malicious nodes, resulting in 51% attacks and affecting system security. The existing singledimensional reputation schemes have the problems of high overhead and insufficient shard consensus in the redistribution process, failing to ensure both performance and security. To address these  problems, a blockchain security sharding scheme based on multidimensional reputation is proposed: Firstly, the scheme integrates multidimensional indicators of nodes to balance shard reputation and computational communication abilities, identifying malicious nodes.  Secondly, a twostage redistribution scheme is proposed to reduce the frequency and cost of redistribution through partial redistribution in first stage and complete redistribution in second stage. Finally, a multidimensional reputation based fast Byzantine faulttolerant consensus (MRFBFT) is designed, which combines voting power and reputation, and introduces consensus among shard leader nodes to prevent malicious behavior. The experimental results show that the shard reputation and computational communication level are more balanced, the consensus delay is reduced by about 20%, and the throughput is increased by about 15%.

Aiming at the problem that existing reversible video information hiding algorithms based on motion vector ordering cannot adaptively adjust the embedding capacity according to the visual characteristics of video frames and have limited capacity, a multipass vector ordering reversible video information hiding algorithm is proposed. This algorithm decides whether to embed information in subsequent frames by assessing the texture and motion complexities of reference frames, thereby enabling adaptive information embedding in subsequent frames. The algorithm also enhances the multipass pixel value ordering (multipass PVO) technique and applies it to video information hiding, significantly enhancing the embedding capacity of reversible hiding algorithms. Experimental results demonstrate that, compared to similar algorithms, the variation values of PSNR and SSIM decreased by 14.5% and 8.5% respectively, and the embedding capacity increased by 7.4%. This represents significant improvements in both visual quality and embedding capacity.

Text desensitization is an extremely important privacy protection method, and the balance between its privacy protection effect and semantic consistency with the original text is a challenge. When existing differential privacy desensitization methods are used to desensitize sensitive words, the similarity calculation probability method is used to select substitute words for sensitive words, which can easily cause inconsistency or even irrelevance between the substitute words and the original text semantics, seriously affecting the preservation of the original text semantics in the desensitized text. A differential privacy text desensitization method is proposed to enhance semantic consistency. A truncation distance measurement formula is given to adjust the probability of selecting replacement words and limit semantic irrelevant replacement words. The experimental results on real datasets show that it effectively improves the semantic consistency between desensitized text and the original text, and has great practical application value.

To address the tradeoff between privacy and utility in differentially private clustering algorithms when handling with massive data, a distributed differentially private Kmeans++ clustering algorithm is proposed. This algorithm leverages the memorybased computing engine Spark to create resilient distributed datasets(RDD) and performs computations using transformation and action operators. During the selection of  initial centroids and iterative updates, a combination of the exponential mechanism and the Laplace mechanism is employed to mitigate the issues of sensitive initial centroids and privacy leakage, while reducing perturbation applied to the data during the computation. According to the characteristics of differential privacy, this paper provides a theoretical proof for the entire algorithm to satisfy εdifferential privacy protection. Experimental results demonstrate that this method possesses excellent privacy protection capabilities and efficient operational efficiency while ensuring the usability of clustering results.

In the digital age, a vast amount of sensitive data is stored across various networks and cloud platforms, making data protection a crucial challenge in the field of information security. Traditional encryption methods are vulnerable due to single point of failure and centralized control, which can lead to data leakage. To address these issues, this study proposes a new method that integrates blockchain technology with an improved proxy reencryption algorithm, utilizing Shamir threshold key sharing. A data sharing scheme TDPRBC based on the threshold proxy reencryption algorithm is designed. Security analysis and experimental results show that this scheme can meet most data access needs.

In recent years, with the increasing number of surveillance cameras and the rapid development of the Internet, there are more and more surveillance and online videos. The automatic detection of behavior conflict in videos is of great significance to reduce the risk of privacy information leakage caused by human auditing, maintain social order and purify the environment online. To fully extract features of behavior conflict from videos and obtain models with good generalization ability and detection performance, we use I3D (inflated 3D convolutional network) and VGGish to extract multimodal features based on the XDViolence dataset, and propose the behavior conflict detection model based on transformer and graph convolution networks (TGBCDM) for behavior conflict detection. The model contains a Transformer encoder module and a graph convolution module, which can effectively capture the longrange dependencies in videos while paying attention to global and local information of video features. After experimental verification, the model outperforms eight existing methods.

Current electronic auctions have the contradictory problems of third party untrustworthiness and the winning bidder’s need to guarantee both anonymity and legitimacy of identity. To address the above problems, a sealed bidding auction scheme based on noninteractive zeroknowledge proof and nonhomogenized tokens is proposed. First, the auction environment of peertopeer participation is constructed through the characteristics of blockchain decentralization and data tampering, and the bidders’ bids are encrypted by elliptic curve+AES algorithm, so as to improve the privacy of the eauction; and then, the generation and verification of the identity legitimacy of the winning bidder in the case of onchain anonymity are realized through noninteractive zeroknowledge proof. At the same time, the identity legitimacy is used as the elemental material of nonhomogenized tokens to ensure the winning bidder’s ownership of the identity legitimacy authentication on the chain; finally, the experimental results prove that the algorithms used outperform the RSA and ECC algorithms in terms of encryption and decryption efficiency, and the required storage overhead for identity authentication is lower than that of other comparative schemes, which meets the needs of daily auctions.

With the rise of renewable energy and the proliferation of distributed power generation, distributed flexible resources capable of bidirectional transmission of electricity and information are becoming increasingly important. In this context, information networks are interconnected wirelessly through the operation of dedicated wireless networks. Due to the vulnerability of the physical layer and the complexity of key distribution and management, it is necessary to consider physical layer security technologies. Traditional physical layer encryption technologies cannot guarantee the security of initial conditions in chaotic systems. To address these issues, we provide a physical layer security scheme for the wireless communication system of distributed flexible resource information networks. This scheme not only reduces the complexity of key distribution and management but also enhances the security of the initial conditions of chaotic systems and the communication system. Our main contributions include a physical layer key generation method that uses lowpass filtering and sliding windows to enhance channel reciprocity, a cumulative distribution function to quantify, and a hash function to achieve key agreement and privacy amplification. We also propose a physical layer encryption scheme based on chaos theory, which uses the previously generated keys to encrypt the phase and amplitude of signals through Arnold transformation and Chen’s chaotic theory. Moreover, we conducted simulations, and the results show that our physical layer key generation scheme enhances the reciprocity of the channel by handling channel characteristics, and the subsequent keys passed the NIST randomness test. Meanwhile, the encryption scheme based on chaotic theory maintains the bit inconsistency rate at 0.5 for eavesdroppers, ensuring the secure transmission of information.

The railway signaling system is the main carrying system of transportation capacity in the current society, and it has extremely high requirements for safety. However, due to the susceptibility of the railway signal system to various external factors and the possibility of malfunctions, it is necessary to design a realtime fault detection scheme for the railway signaling system, so as to take effective maintenance measures. Different from the traditional machine learning fault detection method, the bidirectional encoder representation from transformer (BERT) deep learning model is used for realtime intelligent fault detection. The model can obtain the understanding of the twoway context when dealing with the fault detection task, so as to capture the semantic relationship in the sentence more accurately, and make the understanding of the fault description more accurate. The federated computing method of cloudedge collaboration is adopted, so that the data of each railway operator can be preliminarily processed locally, and then the summarized gradient is uploaded to the cloud for model training. Finally, the trained model parameters are sent back to each edge device to realize the update of the model, breaking through the limitation of scattered training data of the model, and allowing multiple operators to jointly train the BERT model under the premise of maintaining data privacy. The results show that the federated edge cloud computing method for BERT model training can effectively improve the accuracy and reliability of rail transit fault detection while solving the problem of data confidentiality, which is better than the existing fault detection schemes in the field of railway signaling system.

Open source threat intelligence is very important for network security protection, but it has the characteristics of wide distribution, many forms and loud noise. Therefore, how to organize and analyze the collected massive open source threat intelligence efficiently has become an urgent problem to be solved. Therefore, this paper explores a multilabel classification method based on BertTextCNN model, considering the title, text, and regular judgment. According to the characteristics of the text published by the intelligence source, the article sets regular judgment rules to make up for the deficiency of the model. In order to fully reflect the threat topics involved in the open source threat intelligence text, the paper sets the BertTextCNN multilabel classification model for the title and the text respectively, and then resorts the two labels to get the final threat category of the text. Compared with the BertTextCNN multilabel classification model based on text only, the performance of the proposed model is improved, and the recall rate is significantly improved, which can provide valuable reference for the classification of open source threat intelligence.

With the advancement of the information technology revolution represented by artificial intelligence, big data, and the Internet of things, data has become an important asset and driving force for continuous innovation for the country, enterprises, and institutions. Therefore, how to provide a secure channel for terminal access, ensure the secure transmission and storage of business data, prevent data leakage, and have the ability to resist emerging attack technologies such as quantum computing has become an urgent problem that needs to be solved. To address this challenge, this article proposes a software password module based on quantum secure communication technology, and focuses on the design and implementation of quantum software password modules. The focus includes overall architecture design, functional design, and interface integration, as well as testing and verification of the quantum software password module. This module adopts the fusion of quantum cryptography and classical cryptography to provide an efficient and secure solution for the secure transmission and storage of business data, and has broad application scenarios.

Basic software is the cornerstone of supporting the efficient and stable operation of computer systems, which determines the level of development of digital infrastructure. The industrial chain of basic software, represented by operating system, database and middleware, occupies an upstream position in the entire software industry, which directly determines the scale and the efficiency of the downstream output. Due to the characteristics of long R&D cycle and large R&D investment, basic software has gradually attracted attention from various countries and risen to the level of national strategy in the increasingly complex environment of software supply chain. In recent years, while China’s basic software industry has developed rapidly with the help of open source, many security incidents of basic software supply chain have occurred, which brings risks and challenges. This paper reviews the current situation of the basic software supply chain security, analyzes the risks and challenges faced by the basic software supply chain, and puts forward reasonable suggestions from four aspects: policy, industry, user and ecology.