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.

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.

In recent years, the rapid development of blockchain technology has facilitated the transfer of value and asset exchange between different blockchains. However, achieving interoperability among various blockchains necessitates the exploration of cross-chain technology. While cross-chain technology enables asset flow between different blockchains, it also introduces the risk of identity privacy leakage, thus posing a significant threat to user security. To tackle this issue, this article proposes a cross-chain privacy protection scheme that leverages ring signature and relay chain technology. Specifically, this scheme utilizes RCROSS contracts based on ring signatures to handle cross-chain transactions, thereby ensuring the privacy of both parties involved in the transaction. This cross-chain solution demonstrates practicality and efficiency in facilitating cross-chain asset trading. Furthermore, it effectively combats reuse attacks and man-in-the-middle attacks at the application layer while also providing resistance against denial-of-service attacks at the network layer. To validate the proposed cross-chain solution, we conducted tests by constructing a specific cross-chain scenario and by focusing on the natural gas consumption values generated by the RCROSS contract function used in the application chain. The findings indicate that our proposed solution is highly practical in safeguarding the identity privacy of transaction participants. This article’s framework guarantees reliability, security, and efficiency in cross-chain asset transactions. By incorporating ring-based signatures and relay chain technology, users can confidently protect their identity privacy, thus ensuring secure and smooth cross-chain transactions.

With the rapid development of cloud-based data sharing technologies, enterprises and organizations tend to outsource their local data to cloud servers. They adopt searchable encryption (SE) techniques to access and search encrypted data. However, most existing SE schemes use static ranking strategies based on query–index similarity. These strategies fail to capture users’ personalized retrieval preferences and often result in suboptimal search performance. In this article, we present a privacy-preserving data sharing framework with personalized encrypted retrieval (PP-PER) that combines SE technology with federated learning. PP-PER trains user interest models locally on user devices by utilizing historical query behavior. Only encrypted model parameters are uploaded for aggregation, which avoids the centralized collection of users’ private data. In addition, we design an attention-based user query update algorithm. The learned personalized features are integrated into the ciphertext query process. This design enables personalized ranking results and improves the user retrieval experience. Furthermore, PP-PER combines matrix factorization with ciphertext-policy attribute-based encryption (CP-ABE). This mechanism ensures secure document key distribution and supports fine-grained access control. Finally, we formalize the security model under a practical threat and leakage setting and provide a theoretical analysis of the proposed scheme. Experimental results on real-world datasets further validated its practicality and effectiveness.