Design and application of data security privacy protection technology in dance action sequence arrangement and display system

Abstract

In view of the excessive consumption of difficult resources in traditional dance choreography, insufficient data in choreography and data leakage crisis in the system, this paper puts forward artificial intelligence technology to study the protection of dance choreography and system data security and privacy. In this paper, the directed graph neural network is used to intelligently arrange the dance movements, and the Hidden Markov Model (HMM) is used to process the dance scene data so that the output data is highly similar to the original data. In the protection of data security and privacy, the method of data encryption is used, and the encrypted data needs to be decrypted by algorithm for reading. The experimental results show that the more complete the dance movement display, the higher the dance recognition rate, and the recognition rate of the dynamic light projection algorithm is greater than 90 % regardless of several sets of dance movements, while the recognition rate of the traditional light projection algorithm is not up to the standard regardless of several sets of movements, and the recognition rate of the point cloud segmentation method is higher than 90 % only in 3–4 sets of dance and 5–6 sets of dance movements.