Research Article
An Efficient and Privacy-Preserving Physiological Case Classification Scheme for E-healthcare System
@INPROCEEDINGS{10.1007/978-3-030-66922-5_12, author={Gang Shen and Yumin Gui and Mingwu Zhang and Yu Chen and Hanjun Gao and Yixin Su}, title={An Efficient and Privacy-Preserving Physiological Case Classification Scheme for E-healthcare System}, proceedings={Security and Privacy in New Computing Environments. Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, August 6-7, 2020, Proceedings}, proceedings_a={SPNCE}, year={2021}, month={1}, keywords={E-healthcare system Privacy protection Physiological case classification Homomorphic cryptosystem}, doi={10.1007/978-3-030-66922-5_12} }- Gang Shen
Yumin Gui
Mingwu Zhang
Yu Chen
Hanjun Gao
Yixin Su
Year: 2021
An Efficient and Privacy-Preserving Physiological Case Classification Scheme for E-healthcare System
SPNCE
Springer
DOI: 10.1007/978-3-030-66922-5_12
Abstract
In this work, an efficient and privacy-preserving physiological case classification scheme for e-healthcare system (EPPC) is proposed. Specifically, a homomorphic cryptosystem combined with a support vector machine (SVM) algorithm is applied to efficiently classify the physiological cases without compromising patients’ privacy. In terms of the EPPC, it has the capability of diagnosing the patient’s symptom in a timely manner. In addition, a signature authentication technology applied in EPPC can efficiently prevent data from being forged or modified. Security analysis result shows that the proposed EPPC scheme has the following advantages: protect the privacy of patients; ensure that the classification parameters of SVM are secured. Compared with the existing works, the proposed EPPC scheme shows significant advantages in terms of computational costs and communication overheads.
