关键词: 行为量化, 信任值量化, RNN, 模糊理论, HISPAC

Abstract: The current era is computer time, especially the era of artificial intelligence and big data. The emergence of related industries has led to changes in various industries. As major service industry in China, the medical industry is changing quietly. At the some time the protection technology of medical privacy is developed continuously. With the explosrve growth of data, various types of patient identity information, case information and medical diagnosis information are leaked endless. In order to solve the topic of medical privacy protection, the paper constructs a set of medical privacy protection model, which includes two parts：1) An adaptive neural network privacy risk assessment model is constructed by using Recurrent Neural Network (RNN) and fuzzy reasoning theory, which is used to assign a credit label to the user’s behavior and calculate the privacy risk; 2) Based on the user credit risk value obtained from the model, a set of personal privacy data access control mechanism is established, namely HISPAC (Hospital Information System Privacy Access Control Model). The experiment proves that this mechanism has good privacy protection effect and can effectively solve the problem of medical data privacy leakage.

Key words: behavior quantification, quantization of trust value, RNN, fuzzy theory, HISPAC