Publication Type
Journal Article
Version
publishedVersion
Publication Date
1-2021
Abstract
In this paper, we propose a privacy-preserving clinical decision support system using Naïve Bayesian (NB) classifier, hereafter referred to as Peneus, designed for the outsourced cloud computing environment. Peneus allows one to use patient health information to train the NB classifier privately, which can then be used to predict a patient's (undiagnosed) disease based on his/her symptoms in a single communication round. Specifically, we design secure Single Instruction Multiple Data (SIMD) integer circuits using the fully homomorphic encryption scheme, which can greatly increase the performance compared with the original secure integer circuit. Then, we present a privacy-preserving historical Personal Health Information (PHI) aggregation protocol to allow different PHI sources to be securely aggregated without the risk of compromising the privacy of individual data owner. Also, secure NB classifier is constructed to achieve secure disease prediction in the cloud without the help of an additional non-colluding computation server. We then demonstrate that Peneus achieves the goal of patient health status monitoring without privacy leakage to unauthorized parties, as well as the utility and the efficiency of Peneus using simulations and analysis.
Keywords
Servers, Niobium, Cloud Computing, Bayes Methods, Diseases, Encryption, Clinical Decision Support System, Privacy Preserving, Naive Bayesian Classifier, Cloud Computing
Discipline
Information Security
Research Areas
Cybersecurity
Areas of Excellence
Digital transformation
Publication
IEEE Transactions on Services Computing
Volume
12
Issue
1
First Page
222
Last Page
234
ISSN
1939-1374
Identifier
10.1109/TSC.2017.2773604
Publisher
Institute of Electrical and Electronics Engineers
Citation
LIU, Ximeng; DENG, Robert H.; CHOO, Kim-Kwang Raymond; and YANG, Yang.
Privacy-preserving outsourced clinical decision support system in the cloud. (2021). IEEE Transactions on Services Computing. 12, (1), 222-234.
Available at: https://ink.library.smu.edu.sg/sis_research/10134
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1109/TSC.2017.2773604
