Research Article
Research on Cloud Health Privacy Information Protection Algorithm Based on Data Mining
@INPROCEEDINGS{10.1007/978-3-031-33545-7_2, author={Wennan Wang and Shiyang Song and Linkai Zhu and Junyu Su and Te Guo and Jinhai Tang}, title={Research on Cloud Health Privacy Information Protection Algorithm Based on Data Mining}, proceedings={IoT and Big Data Technologies for Health Care. Third EAI International Conference, IoTCare 2022, Virtual Event, December 12-13, 2022, Proceedings}, proceedings_a={IOTCARE}, year={2023}, month={5}, keywords={Data mining Information protection Health privacy Protection algorithm Data set Support}, doi={10.1007/978-3-031-33545-7_2} }- Wennan Wang
Shiyang Song
Linkai Zhu
Junyu Su
Te Guo
Jinhai Tang
Year: 2023
Research on Cloud Health Privacy Information Protection Algorithm Based on Data Mining
IOTCARE
Springer
DOI: 10.1007/978-3-031-33545-7_2
Abstract
In the process of selecting privacy evaluation indicators, the original algorithm did not set the degree of protection risk, which resulted in a little error in the protection of privacy information, which affected the efficiency of data operation. A data mining-based cloud health privacy information protection algorithm was studied. Build a differential privacy information protection model, establish the connection relationship between users and information, and use trapezoidal distribution membership function to determine the sensitive attributes of privacy information. Set up health privacy information protection indicators on the cloud, evaluate the risks of privacy information protection, and classify them into three levels: high, medium and low. Based on data mining, the information privacy mode is extracted, and the minimum support threshold is used to judge the support of private information in the database. Set the privacy information protection algorithm in the way of data support estimation. Experimental results: in the given data set, the support error of the algorithm in this paper is within 1.5%, and the error of the traditional algorithm is more than 5%. With the increasing size of the data set, the execution time of the traditional algorithm is much higher than that of the algorithm in this paper, which shows that the design is effective.
