SecureLR: Secure Logistic Regression Model via a Hybrid Cryptographic Protocol

Machine learning applications are intensively utilized in various science fields, and increasingly the biomedical and healthcare sector. Applying predictive modeling to biomedical data introduces privacy and security concerns requiring additional protection to prevent accidental disclosure or leakag...

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Veröffentlicht in:	IEEE/ACM transactions on computational biology and bioinformatics 2019-01, Vol.16 (1), p.113-123
Hauptverfasser:	Jiang, Yichen, Hamer, Jenny, Wang, Chenghong, Jiang, Xiaoqian, Kim, Miran, Song, Yongsoo, Xia, Yuhou, Mohammed, Noman, Sadat, Md Nazmus, Wang, Shuang
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Artificial intelligence Biological system modeling Biomedical data Biomedical materials Cloud Computing Computational modeling Computer applications Computer Security Cryptography Cybersecurity Data integrity Electronic Health Records Encryption Health care homomorphic en`cryption Learning algorithms Logistic Models Logistic regression Logistics Machine Learning Medical Informatics Prediction models Privacy Regression models secure cloud computing Security SGX Software Storage capacity
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