基于HISPAC医疗数据隐私保护模型

当今时代是计算机的时代，更是人工智能和大数据蓬勃发展的时代，与其相关行业的出现引发了各行各业的变革。作为国内主要的服务行业，医疗产业也在悄然改变，同时医疗隐私的保护技术也在持续研究和发展中。随着数据量的激增，各类患者身份信息、病例信息以及医疗诊断信息泄露的情况层出不穷。本文针对医疗隐私保护问题，构建一套医疗隐私保护模型，该模型包括2个部分:1)借助循环神经网络RNN和模糊推理理论构建一个自适应神经网络隐私风险评估模型，给用户行为活动设置一个信用标签，并借此来计算隐私泄露风险值; 2)围绕模型得到的用户信用风险值建立一套个性化的隐私数据访问权限控制机制，即医院信息系统隐私控制模型。经过实验验证，该机制具有良好的隐私保护效果，可以有效解决医疗数据隐私泄露的问题。

Privacy Protection Model for Medical Data Based on HISPAC

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.