کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4950209 | 1364281 | 2018 | 50 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Hybrid privacy-preserving clinical decision support system in fog-cloud computing
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نظریه محاسباتی و ریاضیات
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چکیده انگلیسی
In this paper, we propose a framework for hybrid privacy-preserving clinical decision support system in fog-cloud computing, called HPCS. In HPCS, a fog server uses a lightweight data mining method to securely monitor patients' health condition in real-time. The newly detected abnormal symptoms can be further sent to the cloud server for high-accuracy prediction in a privacy-preserving way. Specifically, for the fog servers, we design a new secure outsourced inner-product protocol for achieving secure lightweight single-layer neural network. Also, a privacy-preserving piecewise polynomial calculation protocol allows cloud server to securely perform any activation functions in multiple-layer neural network. Moreover, to solve the computation overflow problem, a new protocol called privacy-preserving fraction approximation protocol is designed. We then prove that the HPCS achieves the goal of patient health status monitoring without privacy leakage to unauthorized parties by balancing real-time and high-accurate prediction using simulations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 78, Part 2, January 2018, Pages 825-837
Journal: Future Generation Computer Systems - Volume 78, Part 2, January 2018, Pages 825-837
نویسندگان
Ximeng Liu, Robert H. Deng, Yang Yang, Hieu N. Tran, Shangping Zhong,