Construction and Management of Doctor-Patient Privacy Protection System under Big Data Computing Environment

In the era of digital healthcare, protecting the privacy of doctors and patients has become an important challenge. The construction and management of privacy protection systems requires an integrated approach to ensure that the collection, storage and use of patient information both meets medical needs and maximizes the protection of physician and patient privacy. Therefore, this paper makes an in-depth analysis of the doctor-patient privacy protection system, describes and experiments the algorithms involved, and aims to improve the privacy protection system. This paper mainly uses experimental method and comparative method to design and analyze the privacy protection algorithm. The experimental results show that the information loss of the nearest far-point method is the largest when k value is 25, and the accuracy of a single algorithm in the system is difficult to meet the requirements, so it is necessary to add a transform-based mixed data micro-aggregation method to improve the performance of the system. This paper found that the privacy risk of medical data increases in the big data computing environment. Due to the large scale of data and the need for data sharing, doctor-patient privacy is even more threatened. Different privacy protection technologies, such as data desensitization, encryption and access control, can effectively reduce the risk of doctor-patient privacy leakage. There is a trade-off between privacy and data utility. Improving privacy protection may reduce the analyability and utility of data.