Haoyang Yu

Quantum computing poses a formidable threat to classical cryptographic systems, particularly those safeguarding blockchain-based transactions and user privacy. Conventional algorithms such as RSA and elliptic curve cryptography are vulnerable to quantum attacks through Shor's and Grover's algorithms, jeopardizing the long-term security of digital assets. To address these challenges, this paper proposes a Lightweight Lattice-based Privacy Protection (LLPP) blockchain framework that integrates lattice-based digital signatures with a double encryption mechanism. The framework is designed to provide post-quantum security for transaction authentication while enhancing payment address privacy and reducing direct linkage under public observation, even in the presence of quantum-capable adversaries. A key challenge in deploying lattice-based cryptography on blockchain systems is the substantial storage overhead of signatures and public keys. LLPP addresses this by leveraging the InterPlanetary File System (IPFS) for off-chain storage, recording only 46-byte content identifiers on-chain while maintaining full verifiability. Comprehensive evaluations on the Substrate platform demonstrate that LLPP provides practical post-quantum authentication and privacy protection with signing and verification latencies below 2 milliseconds, making it practical for deployment in quantum-secure blockchain environments.

The widespread adoption of consumer electronics has enabled patients to easily schedule appointments with doctors through online platforms, advancing the development of telemedicine. Utilizing AI technology, these platforms assist physicians in symptom analysis and providing precise recommendations, overcoming geographical barriers and achieving a balanced utilization of medical resources. Moreover, the advancement of 5G and the anticipated 6G networks has provided the capability for swift and accurate execution of cloud-based AI applications. However, online medical image consultations based on plaintext carry the risk of significant privacy breaches. By leveraging secret sharing technology, privacy leakage issues can be effectively addressed. However, the application of this technology alters the visual representation of image data. In multi-party scenarios, each party possesses only one secret share, which makes verifying the authenticity of the image challenging. To address this issue, we propose the Secure Federated Image Authentication system, which enables the authentication of encrypted images provided by consultants before the provision of services. This system employs technologies such as random feature signatures, secret sharing, and distributed reversible data hiding to achieve federated authentication of images. Through security analysis and performance evaluation, our method has demonstrated satisfactory results in terms of security and availability.

Image dehazing represents a dynamic area of research in computer vision. With the exponential development of deep learning, particularly convolutional neural networks (CNNs), innovative and effective image dehazing techniques have surfaced. However, in stark contrast to the majority of computer vision tasks employing CNNs, the output from a dehazing model is often treated as uninformative noise, even though the model’s filters are engineered to extract pertinent features from the images. The standard approach of end-to-end models for dehazing involves noise removal from the hazy image to obtain a clear one. Consequently, the model’s dehazing capacity diminishes, as the noise is progressively filtered out throughout the propagation phase. This leads to the conception of the feature reduction network (FRNet), which is a distinctive CNN architecture that incrementally eliminates informative features, thereby resulting in the output of noise. Our experimental results indicate that the CNN-driven FRNet surpasses previous state-of-the-art (SOTA) methods in terms of the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) evaluation metrics. This highlights the effectiveness of the FRNet across various image dehazing datasets. With its reduced overhead, the CNN-based FRNet demonstrates superior performance over current SOTA methods, thereby affirming the efficacy of CNNs in image dehazing tasks.