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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
  • 32 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1227)

Abstract

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.

Keywords

PPDP SIFT CNN 

References

  1. 1.
    Alakwaa, W., Nassef, M., Badr, A.: Lung cancer detection and classification with 3D Convolutional Neural Network (3D-CNN). Int. J. Adv. Comput. Sci. Appl. 8, 409 (2017)Google Scholar
  2. 2.
    Sun, W., Zheng, B., Qian, W.: Computer aided lung cancer diagnosis with deep learning algorithms. In: Medical Imaging 2016. Computer-Aided Diagnosis, vol. 9785, p. 97850Z (2016)Google Scholar
  3. 3.
    Warner, S.L.: Randomized response: a survey technique for eliminating evasive answer bias. J. Am. Stat. Assoc. 60(309), 63–69 (1965)CrossRefGoogle Scholar
  4. 4.
    Greenberg, B.G., Abul-Ela, A.L.A., Simmons, W.R., Horvitz, D.G.: The unrelated question randomized response model: theoretical framework. J. Am. Stat. Assoc. 64, 520–539 (1969)MathSciNetCrossRefGoogle Scholar
  5. 5.
    David, C.: Untraceable electronic mail, return addresses, and digital pseudonyms. In: Secure Electronic Voting, pp. 84–90 (1981)Google Scholar
  6. 6.
    Yang, Z., Zhong, S., Wright, R.N.: Anonymity-preserving data collection. In: Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp. 334–343 (2005)Google Scholar
  7. 7.
    Mendes, R., Vilela, J.P.: Privacy-preserving data mining: methods, metrics, and applications. IEEE Access 5, 10562–10582 (2017)CrossRefGoogle Scholar
  8. 8.
    Samarati, P., Sweeney, L.: Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppresion. In: Proceedings of the IEEE Symposium on Research in Security and Privacy, pp. 384–393 (1998)Google Scholar
  9. 9.
    Aggarwal, C.C., Yu, P.S.: A general survey of privacy-preserving data mining models and algorithms. In: Aggarwal, C.C., Yu, P.S. (eds.) privacy-preserving data mining, pp. 11–52. Springer, Boston (2008).  https://doi.org/10.1007/978-0-387-70992-5_2CrossRefGoogle Scholar
  10. 10.
    Li, N., Li, T., Venkatasubramanian, S.: t-closeness: privacy beyond k-anonymity and l-diversity. In: 2007 IEEE 23rd International Conference on Data Engineering, pp. 106–115 (2007)Google Scholar
  11. 11.
    Lindeberg, T.: Scale invariant feature transform. Scholarpedia 7, 10491 (2012)CrossRefGoogle Scholar
  12. 12.
    Lowe, D.G.: Object recognition from local scale-invariant features. In: Proceedings of the Seventh IEEE International Conference on Computer Vision, pp. 1150–1157 (1999)Google Scholar
  13. 13.
    Cheung, W., Hannarneh, G.: N-SIFT: N-dimensional scale invariant feature transform for matching medical images. In: 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, pp. 720–723 (2007). Bioinformatics University of British Columbia Medical Image Analysis LabGoogle Scholar
  14. 14.
    LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1, 541–551 (1989)CrossRefGoogle Scholar
  15. 15.
    LeCun, Y.: Generalization and network design strategies. In: Connectionism in Perspective, pp. 143–155 (1989)Google Scholar
  16. 16.
    Krizhevsky, A., Sutskever, I., Hinton, G.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)Google Scholar
  17. 17.
    Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 580–587 (2014)Google Scholar
  18. 18.
    Li, Q., Cai, W., Wang, X., Zhou, Y., Feng, D.D., Chen, M.: Medical image classification with convolutional neural network. In: 2014 13th International Conference on Control Automation Robotics and Vision, ICARCV 2014, pp. 844–848 (2014)Google Scholar

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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