## 1 Introduction
The independent vector analysis (IVA) [24] algorithm has been proposed as an extension of the ICA algorithm, which is mainly used to solve the permutation ambiguity problem in FD-CBSS. Considering the algorithm itself, the traditional independent univariate source prior is replaced by the source prior knowledge of multivariate correlations, using higher-order correlations across frequencies [25] to prioritize the associated multivariate super-Gaussian distribution over each vector source. This modeling imposes the independence of sources between vectors while preserving the correlation of sources within higher-order vectors, i.e., the structural correlation between the frequency components of each source. The dependent multi-source prior preserves the dependence between the different frequency units of each source and maximizes the independence between the frequency units of the different sources [26]. Thus, the IVA algorithm alleviates the permutation ambiguity problem in the learning process and does not require subsequent permutation processing. To improve the separation performance and faster convergence of this algorithm, the fast fixed-point IVA (Fast IVA) method [27] of the Newtonian learning algorithm is applied to better solve the case of CBSS. This has been studied in more depth by subsequent scholars. [PERSON] proposed an online IVA algorithm for real-time audio separation and developed a two-channel hardware demonstration system [28]. [PERSON] applied the IVA model to solve the joint blind source separation problem for fMRI data and in the process derived gradient and Newton-based update rules [29]. [PERSON] used video information to provide an intelligent initialization for the optimization problem and proposed a fast video-based fixed-point analysis method that still has good separation performance in noisy environments [30]. To this end, this paper designed a speech information security transmission under chaotic masking and a FD-CBSS algorithm based on Fast IVA. The scheme is designed for the security of speech signals in wireless transmission environments. First, the speech signals to be transmitted were processed by chaotic masking and then sent to the channel for multipath transmission. Then, the encrypted convolutional speech signal in the time domain was transformed into the frequency domain using the short-time Fourier transform (STFT), followed by blind signal extraction using the Fast IVA algorithm. The blind deconvolution process of the signal was completed when the separation matrix converged or when the number of iterations was reached. The algorithm is able to perform permutation simultaneously during the iterative update of the separation matrix and converges quickly. This paper is organized as follows: in the second part, Chen chaotic systems for confidential transmission are firstly briefly introduced; and then the FD-CBS under multipath transmission model is highlighted. In the third part, the theoretical basis of the Fast IVA algorithm is first introduced, and the specific implementation steps of the algorithm are listed. Furthermore, a safe and fast overall process of blind deconvolution based on Fast IVA algorithm is illustrated. Finally, the evaluation criteria used to judge the separation performance of the algorithm are presented. The fourth part is to simulate the algorithm experiment and analyze the algorithm performance. In the fifth part, the conclusions and future research prospects are given based on the analysis results.