PPCD: Privacy-preserving clinical decision with cloud support

PLoS One. 2019 May 29;14(5):e0217349. doi: 10.1371/journal.pone.0217349. eCollection 2019.

Abstract

With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other's private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Cloud Computing
  • Computer Security / trends
  • Confidentiality / ethics*
  • Confidentiality / standards
  • Decision Making
  • Decision Making, Computer-Assisted*
  • Disclosure
  • Electronic Health Records
  • Humans
  • Machine Learning
  • Medical Records
  • Privacy

Grants and funding

This work is supported by the National Key R&D Program of China under Grants no. 2017YFB0802000 to BW, the National Natural Science Foundation of China under Grant no. U1736111 to BW, the Plan For Scientific Innovation Talent of Henan Province under Grand no. 184100510012 to BW, the Program for Science & Technology Innovation Talents in Universities of He’nan Province under Grant No. 18HASTIT022 to YP, Key Technologies R&D Program of He’nan Province under Grant No. 182102210123 to YP, the Foundation of He’nan Educational Committee under Grant No. 18A520047 to YP, the Foundation for University Key Teacher of He’nan Province under Grant No. 2016GGJS-141 to YP, Key Technologies R&D Program of He’nan Province (192102210295 to HM), and Innovation Scientists and Technicians Troop Construction Projects of Henan Province. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.