Epistasis Detection (ED) was widely used for identifying potential risk disease variants in the human genome. A statistically meaningful ED typically requires a more extensive dataset to detect complex disease-associated Single Nucleotide Polymorphisms (SNPs), but a single institution generally possesses limited genome data. Thus, it is necessary to collect multi-institutional genome data to carry out research together. However, concerns regarding privacy and trustworthiness impede the sharing of massive genome data. Therefore, this study proposes a novel federated ED framework with the sequence perturbation privacy-preserving method to address the limitation of distributed data sharing (FedED-SegNAS). Firstly, to address the lack of interpretability in deep learning models, integrate fuzzy logic into Convolutional Neural Networks (CNNs), promoting the capabilities of CNN to represent the ambiguities of genomic data with high interpretability and reasonable accuracy. Secondly, consider using the Neural Architecture Search (NAS) method to optimize the federated neural architecture. Specifically, selecting the Particle Swarm Optimization (PSO) algorithm to automatically search the optimal neural architecture at different stages in federated learning based on adaptive multi-objectives decreases the communication cost and improves communication efficiency. Furthermore, to ensure the security of the parameter transfer process, design the sequence perturbation privacy-preserving method, grouping the upload and download parameters of federated learning and randomly perturbing the group number so that the attacker cannot obtain the corresponding result between the group number and parameters. Its rationality and security have been proven. The experiments conducted on a range of datasets demonstrate the superiority of the framework over state-of-the-art epistasis detection methods. FedED-SegNAS can reduce network complexity while protecting genome data security.

This paper presents a new image encryption solution using the chaotic Josephus matrix. It extends the conventional Josephus traversing to a matrix form and proposes a treatment to improve the randomness of this matrix by mixing chaotic maps. It also derives the corresponding encryption primitives controlled by the chaotic Josephus matrix. In this way, it builds up an image encryption system with very high sensitivities in both encryption key and input image. Our simulation results demonstrate that an encrypted image of using this method is very random‐like, that is, a uniform‐like pixel histogram and very low correlations in adjacent pixels. The design idea of this method is also applicable to data encryption of other types, like audio and video.