Multi-modal Big Data Security Fusion Method Based on Privacy ASE Encryption Algorithm and Width Learning

The network is open, which makes it vulnerable to attacks and reduces the accuracy of multi-modal big data security fusion. To solve this problem, a multi-modal big data security fusion method based on privacy ASE encryption algorithm and width learning is proposed. Based on the privacy ASE encryption algorithm, each type of multimodal data is defined, and the corresponding attributes are defined, and the key tree of multimodal big data security fusion is constructed. According to the results of multi-modal big data security fusion based on privacy ASE encryption algorithm, the width learning technology is adopted to continue the fusion processing, generate a key protocol, divide normal nodes and malicious nodes, eliminate the information of malicious nodes, calculate the weights of multi-modal big data information fusion, set constraints, and get the results of multi-modal big data information security fusion. The experimental results show that the correlation coefficient of adjacent data managed by this method is low, and the average correlation coefficient is 0.037, so the data security is high. There is a small gap between the obtained fusion results and the ideal results, which effectively guarantees the credibility of the fusion process.