The privacy-preserving data publishing in medical application: A survey

Chieh Lin Chuang, Pang Chieh Wang, Ming-Shi Wang, Chin Feng Lai

Research output: Chapter in Book/Report/Conference proceedingConference contribution


As the time goes by and the development of science and technology, medical issues are becoming part of everyone’s life. People are paying much more attention to their own rights. When people transfer from one hospital to another for treatment, they may need to repeat multiple examinations, resulting unnecessary expenses and waste of medical resources. The Ministry of Health and Welfare promotes a system that uploads all patients’ medical files to an online database allowing them to check their own medical information and share it also, but it probably makes some privacy problem. In this paper, we found several methods that might be useful for image encryption, including, privacy-preserving data publishing (PPDP), scale invariant feature transform (SIFT), convolutional neural network (CNN), explored their algorithms and compare their strengths and weaknesses in the field of medical imaging encryption. Hoping to find a method for people to use the system without doubt.

Original languageEnglish
Title of host publicationCognitive Cities - 2nd International Conference, IC3 2019, Revised Selected Papers
EditorsJian Shen, Yao-Chung Chang, Yu-Sheng Su, Hiroaki Ogata
Number of pages10
ISBN (Print)9789811561122
Publication statusPublished - 2020
Event2nd International Cognitive Cities Conference, IC3 2019 - Kyoto, Japan
Duration: 2019 Sep 32019 Sep 6

Publication series

NameCommunications in Computer and Information Science
Volume1227 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference2nd International Cognitive Cities Conference, IC3 2019

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

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