Collaborative Secure Decision Tree Training for Heart Disease Diagnosis in Internet of Medical Things
0
조회
0
다운로드
0
인용
0
참고문헌
키워드
초록
In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to beretained locally. The training model of heart disease data can predict patients' physical health status effectively,thereby providing reliable disease information. It is crucial to make full use of multiple data sources in theInternet of Medical Things applications to improve model accuracy. As network communication speeds andcomputational capabilities continue to evolve, parties are storing data locally, and using privacy protectiontechnology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in thehealthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper,we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease datasetto address the challenges associated with developing a practical and usable model while ensuring the protectionof heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method isas high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.
Abstract
In the Internet of Medical Things, due to the sensitivity of medical information, data typically need to beretained locally. The training model of heart disease data can predict patients' physical health status effectively,thereby providing reliable disease information. It is crucial to make full use of multiple data sources in theInternet of Medical Things applications to improve model accuracy. As network communication speeds andcomputational capabilities continue to evolve, parties are storing data locally, and using privacy protectiontechnology to exchange data in the communication process to construct models is receiving increasing attention. This shift toward secure and efficient data collaboration is expected to revolutionize computer modeling in thehealthcare field by ensuring accuracy and privacy in the analysis of critical medical information. In this paper,we train and test a multiparty decision tree model for the Internet of Medical Things on a heart disease datasetto address the challenges associated with developing a practical and usable model while ensuring the protectionof heart disease data. Experimental results demonstrate that the accuracy of our privacy protection method isas high as 93.24%, representing a difference of only 0.3% compared with a conventional plaintext algorithm.
참고문헌 (8)
- S. S. Alotaibi, "Quantum-enhanced machine learning algorithms for heart disease prediction", Human-centric Computing and Information Sciences, 13 (2023)[DOI]
- D. Maheshwari, "Quantum machine learning applied to electronic healthcare records for ischemic heart disease classification", Human-centric Computing and Information Sciences, 13 (2023)[DOI]
- K. Cheng, "SecureBoost : a lossless federated learning framework", IEEE Intelligent Systems, 36(6), pp.87~98 (2021)[DOI]
- W. J. Lu, "Squirrel : a scalable secure two-party computation framework for training gradient boosting decision tree", Proceedings of the 32nd USENIX Security Symposium(USENIX Security 23), pp.6435~6451 (2023)
- S. Wagh, "Falcon : honest-majority maliciously secure framework for private deep learning", Proceedings on Privacy Enhancing Technologies, 2021(1), pp.188~208 (2021)[DOI]
- K. Hamada, "Efficient decision tree training with new data structure for secure multi-party computation", Proceedings on Privacy Enhancing Technologies, 2023(1), pp.343~364 (2023)[DOI]
- M. Abspoel, "Secure training of decision trees with continuous attributes", Proceedings on Privacy Enhancing Technologies, 2021(1), pp.167~187 (2021)[DOI]
- M. Keller, "MP-SPDZ : a versatile framework for multi-party computation", Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security, pp.1575~1590 (2020)