The Privacy-Preserving Data Publishing in Medical Application: A Survey

  • Chieh-Lin ChuangEmail author
  • Pang-Chieh Wang
  • Ming-Shi Wang
  • Chin-Feng Lai
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1227)


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.




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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Chieh-Lin Chuang
    • 1
    Email author
  • Pang-Chieh Wang
    • 2
  • Ming-Shi Wang
    • 1
  • Chin-Feng Lai
    • 1
  1. 1.National Cheng Kung UniversityTainanTaiwan (R.O.C.)
  2. 2.Hsinchu CountyTaiwan (R.O.C.)

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