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As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender's identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender's identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, clients and users with limited computing resources can outsource their computation-intensive tasks to the Cloud Service Providers (CSPs). However, as the CSPs are commercial in nature and aim to increase their profits, some security challenges are still attached to them. In this paper, we propose an efficient publicly verifiable computation scheme (EPVM) for large-scale matrix multiplication with privacy preservation. Based on the theory of discrete logarithm problem and the techniques of privacy-preserving matrix transformation, our scheme not only protects the privacy of the client’s matrices but also significantly reduces the computation overhead on the client end as well as the CSP side. Our detailed security analysis and proofs show that the proposed scheme can achieve the established security requirements. The experimental evaluation also demonstrates that the proposed scheme works efficiently as compared to other existing solutions. [ABSTRACT FROM AUTHOR]

The advancements and adoption of cloud-assisted ehealthcare systems have enabled the storage of massive electronic medical records (EMRs) in the cloud for efficient and easy access. A direct benefit of EMRs is the ability of patients to search for EMRs that are similar to their own in the cloud for use as references. These similar EMRs can help a patient find appropriate medical services quickly. However, for large-scale ehealthcare systems, challenges remain with respect to ensuring the efficiency and privacy of these queries. In this study, we construct an efficient and privacy-preserving similar EMR query scheme to help patients find similar EMRs to reference in a large-scale ehealthcare system. Specifically, we propose a coarse-grained query method based on a binary decision tree to find a set of EMRs corresponding to the patient's set of medical-symptom keywords. We also design a fine-grained query method to find similar EMRs that meet the threshold set by the patient. A detailed security analysis shows that the proposed scheme is secure. The efficiency of the proposed method in a large-scale ehealthcare system is verified experimentally. • An efficient and privacy-preserving similar EMRs query scheme is proposed. • A novel privacy-preserving health symptom keywords match query protocol is designed. • Security analysis demonstrates the security of the proposed scheme. • Experimental simulations show the efficiency of the scheme. [ABSTRACT FROM AUTHOR]