Federated learning not only realizes collaborative training of models, but also effectively maintains user privacy. However, with the widespread application of privacy-preserving federated learning, poisoning attacks threaten the model utility. Existing defense schemes suffer from a series of problems, including low accuracy, low robustness and reliance on strong assumptions, which limit the practicability of federated learning. To solve these problems, we propose a Robustness-enhanced privacy-preserving Federated learning with scaled dot-product attention (RFed) under dual-server model. Specifically, we design a highly robust defense mechanism that uses a dual-server model instead of traditional single-server model to significantly improve model accuracy and completely eliminate the reliance on strong assumptions. Formal security analysis proves that our scheme achieves convergence and provides privacy protection, and extensive experiments demonstrate that our scheme reduces high computational overhead while guaranteeing privacy preservation and model accuracy, and ensures that the failure rate of poisoning attacks is higher than 96%.

Due to the rapid emergence of malware and its greater harm, the successful execution of malware often brings incalculable losses. Consequently, the detection of malware has become increasingly crucial. The sequence of API calls in software embodies substantial behavioral information, offering significant advantages in the identification of malicious activities. Meanwhile, the capability of automatic feature extraction by deep learning can better mine the features of API call sequences. In the current research, API features remain underutilized, resulting in suboptimal accuracy in API detection. In this paper, we propose a deep-learning-based method for detecting malware using API call sequences. This method transforms the API call sequence into a grayscale image and performs classification in conjunction with sequence features. By leveraging a range of deep-learning algorithms, we extract diverse behavioral information from software, encompassing semantic details, time-series information, API call frequency data, and more. Additionally, we introduce a specialized neural network framework and assess the impact of pixel size on classification effectiveness during the grayscale image-mapping process. The experimental results show that the accuracy of our classification method is as high as 99%. Compared with other malware-detection techniques, especially those based on API call sequences, our method maps API call sequences to gray image analysis and has higher detection accuracy.

Digital watermarking technology and watermark attack methods are mutually reinforcing and complementary. Currently, traditional watermark attack methods are relatively mature, but these traditional attack methods will inevitably damage the visual quality of original images (OIs). Therefore, this paper proposes a covert attack method called residual dense based imperceptible watermark attack network (RD-IWAN). First, this paper designs a watermark attack residual dense network (WARDN) based on the residual dense network (RDN), which can effectively remove the watermark information in the middle and low frequency features of the watermarked image (WMI). Second, to improve the attack ability of the network, this paper innovatively proposes a progressive preprocessing method based on the information enhancement preprocessing method. Concurrently, to ensure the imperceptibility of this watermark attack method, a comprehensive loss function that combines the perceptual loss and mean square error loss (MSE) of OI and attacked watermarked image (AWMI) is designed in this study. Finally, attack experiments are designed and performed on watermarks with different embedding strengths and sizes. Experimental results show that, compared to traditional attack methods, the watermark attack method proposed in this paper exhibits stronger attack ability and higher imperceptibility.

With the rapid development of artificial intelligence (AI) in the healthcare industry, the sharing of personal healthcare data plays an essential role in advancing medical AI. Unfortunately, personal healthcare data sharing is plagued by challenges like ambiguous data ownership and privacy leakage. Blockchain, which stores the hash of shared data on-chain and ciphertext off-chain, is treated as a promising approach to address the above issues. However, this approach lacks a flexible and reliable mechanism for incremental updates of the same case data. To avoid the overhead of authentication, access control, and rewards caused by on-chain data changes, we propose a blockchain and trusted execution environment (TEE)-based privacy-preserving sharing scheme for healthcare data that supports incremental updates. Based on chameleon hash and TEE, the scheme achieves reliable incremental updates and verification without changing the on-chain data. In the scheme, for privacy concerns, off-chain data are protected through symmetric encryption, whereas data verification, decryption, and computation are performed within TEE. The experimental results show the feasibility and effectiveness of the proposed scheme.

To address the challenges posed by data silos and heterogeneity in distributed machine learning, privacy-preserving asynchronous Federated Learning (FL) has been extensively explored in academic and industrial fields. However, existing privacy-preserving asynchronous FL schemes still suffer from the problem of low model accuracy caused by inconsistency between delayed model updates and current model updates, and even cannot adapt well to Non-Independent and Identically Distributed (Non-IID) settings. To address these issues, we propose a Privacy-preserving Asynchronous Federated Learning based on the alternating direction multiplier method (PAFed), which is able to achieve high-accuracy models in Non-IID settings. Specifically, we utilize vector projection techniques to correct the inconsistency between delayed model updates and current model updates, thereby reducing the impact of delayed model updates on the aggregation of current model updates. Additionally, we employ an optimization method based on alternating direction multipliers to adapt the Non-IID settings to further enhance the global model accuracy. Finally, through extensive experiments, we demonstrate that our scheme improves the model accuracy by up to 12.53% when compared with current state-of-the-art solution FedADMM.