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Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives

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Trustworthy Machine Learning for Healthcare (TML4H 2023)

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Abstract

Machine Learning (ML) has recently shown tremendous success in modeling various healthcare prediction tasks, ranging from disease diagnosis and prognosis to patient treatment. Due to the sensitive nature of medical data, privacy must be considered along the entire ML pipeline, from model training to inference. In this paper, we conduct a review of recent literature concerning Privacy-Preserving Machine Learning (PPML) for healthcare. We primarily focus on privacy-preserving training and inference-as-a-service, and perform a comprehensive review of existing trends, identify challenges, and discuss opportunities for future research directions. The aim of this review is to guide the development of private and efficient ML models in healthcare, with the prospects of translating research efforts into real-world settings.

A. Guerra-Manzanares and L. J. L. Lopez—Equal contributions.

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References

  1. Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp. 308–318 (2016)

    Google Scholar 

  2. Acar, A., Aksu, H., Uluagac, A.S., Conti, M.: A survey on homomorphic encryption schemes: theory and implementation. ACM Comput. Surv. (Csur) 51(4), 1–35 (2018)

    Article  Google Scholar 

  3. Ali, M., Naeem, F., Tariq, M., Kaddoum, G.: Federated learning for privacy preservation in smart healthcare systems: a comprehensive survey. IEEE J. Biomed. Health Inform. (2022)

    Google Scholar 

  4. Baruch, M., Drucker, N., Greenberg, L., Moshkowich, G.: A methodology for training homomorphic encryption friendly neural networks. In: Zhou, J., et al. (eds.) ACNS 2022. LNCS, vol. 13285, pp. 536–553. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16815-4_29

    Chapter  Google Scholar 

  5. Boulila, W., Ammar, A., Benjdira, B., Koubaa, A.: Securing the classification of COVID-19 in chest x-ray images: a privacy-preserving deep learning approach. In: 2022 2nd International Conference of Smart Systems and Emerging Technologies (SMARTTECH), pp. 220–225. IEEE (2022)

    Google Scholar 

  6. Chen, Y., Mao, Q., Wang, B., Duan, P., Zhang, B., Hong, Z.: Privacy-preserving multi-class support vector machine model on medical diagnosis. IEEE J. Biomed. Health Inform. 26(7), 3342–3353 (2022)

    Article  Google Scholar 

  7. Chilukoti, V.S.T.S.V., Hsu, S., Hei, X.: Privacy-preserving deep learning model for COVID-19 disease detection. arXiv preprint arXiv:2209.04445 (2022)

  8. Dayan, I., et al.: Federated learning for predicting clinical outcomes in patients with COVID-19. Nat. Med. 27(10), 1735–1743 (2021)

    Article  Google Scholar 

  9. Deist, T.M., et al.: Distributed learning on 20 000+ lung cancer patients-the personal health train. Radiother. Oncol. 144, 189–200 (2020)

    Article  Google Scholar 

  10. Dosovitskiy, A., et al.: An image is worth 16\(\times \)16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

  11. Dou, Q., et al.: Federated deep learning for detecting COVID-19 lung abnormalities in CT: a privacy-preserving multinational validation study. NPJ Digit. Med. 4(1), 60 (2021)

    Article  Google Scholar 

  12. Dwork, C., Roth, A., et al.: The algorithmic foundations of differential privacy. Found. Trends® Theor. Comput. Sci. 9(3–4), 211–407 (2014)

    Google Scholar 

  13. Festag, S., Spreckelsen, C.: Privacy-preserving deep learning for the detection of protected health information in real-world data: comparative evaluation. JMIR Format. Res. 4(5), e14064 (2020)

    Google Scholar 

  14. Field, M., et al.: Infrastructure platform for privacy-preserving distributed machine learning development of computer-assisted theragnostics in cancer. J. Biomed. Inform. 134, 104181 (2022)

    Google Scholar 

  15. Goldreich, O.: Secure multi-party computation. Manuscript. Preliminary version 78(110) (1998)

    Google Scholar 

  16. Gopalakrishnan, A., Kulkarni, N.P., Raghavendra, C., Manjappa, R., Honnavalli, P.B., Eswaran, S.: PriMed: private federated training and encrypted inference on medical images in healthcare. Available at SSRN 4196696 (2021)

    Google Scholar 

  17. Gostin, L.O., Levit, L.A., Nass, S.J., et al.: Beyond the HIPAA privacy rule: enhancing privacy, improving health through research (2009)

    Google Scholar 

  18. Hong, C., et al.: Privacy-preserving collaborative machine learning on genomic data using TensorFlow. In: Proceedings of the ACM Turing Celebration Conference-China, pp. 39–44 (2020)

    Google Scholar 

  19. Huang, Q.X., Yap, W.L., Chiu, M.Y., Sun, H.M.: Privacy-preserving deep learning with learnable image encryption on medical images. IEEE Access 10, 66345–66355 (2022)

    Article  Google Scholar 

  20. Islam, H., Alaboud, K., Paul, T., Rana, M.K.Z., Mosa, A.: A privacy-preserved transfer learning concept to predict diabetic kidney disease at out-of-network siloed sites using an in-network federated model on real-world data. In: AMIA Annual Symposium Proceedings, vol. 2022, p. 264. American Medical Informatics Association (2022)

    Google Scholar 

  21. Islam, T.U., Ghasemi, R., Mohammed, N.: Privacy-preserving federated learning model for healthcare data. In: 2022 IEEE 12th Annual Computing and Communication Workshop and Conference (CCWC), pp. 0281–0287. IEEE (2022)

    Google Scholar 

  22. Jarin, I., Eshete, B.: PRICURE: privacy-preserving collaborative inference in a multi-party setting. In: Proceedings of the 2021 ACM Workshop on Security and Privacy Analytics, pp. 25–35 (2021)

    Google Scholar 

  23. Javaid, M., Haleem, A., Singh, R.P., Suman, R., Rab, S.: Significance of machine learning in healthcare: features, pillars and applications. Int. J. Intell. Netw. 3, 58–73 (2022)

    Google Scholar 

  24. Joshi, M., Pal, A., Sankarasubbu, M.: Federated learning for healthcare domain-pipeline, applications and challenges. ACM Trans. Comput. Healthc. 3(4), 1–36 (2022)

    Article  Google Scholar 

  25. Jumper, J., et al.: Highly accurate protein structure prediction with alphafold. Nature 596(7873), 583–589 (2021)

    Article  Google Scholar 

  26. Kaissis, G., Ziller, A., et al.: End-to-end privacy preserving deep learning on multi-institutional medical imaging. Nat. Mach. Intell. 3(6), 473–484 (2021)

    Article  Google Scholar 

  27. Kasyap, H., Tripathy, S.: Privacy-preserving decentralized learning framework for healthcare system. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 17(2s), 1–24 (2021)

    Article  Google Scholar 

  28. Kaul, D., Raju, H., Tripathy, B.: Deep learning in healthcare. Deep Learning in Data Analytics: Recent Techniques, Practices and Applications, pp. 97–115 (2022)

    Google Scholar 

  29. Kerkouche, R., Acs, G., Castelluccia, C., Genevès, P.: Privacy-preserving and bandwidth-efficient federated learning: an application to in-hospital mortality prediction. In: Proceedings of the Conference on Health, Inference, and Learning, pp. 25–35 (2021)

    Google Scholar 

  30. Kreuzberger, D., Kühl, N., Hirschl, S.: Machine learning operations (MLOps): overview, definition, and architecture. arXiv preprint arXiv:2205.02302 (2022)

  31. Lee, G.H., Shin, S.Y.: Federated learning on clinical benchmark data: performance assessment. J. Med. Internet Res. 22(10), e20891 (2020)

    Google Scholar 

  32. Lins, S., Pandl, K.D., Teigeler, H., Thiebes, S., Bayer, C., Sunyaev, A.: Artificial intelligence as a service: classification and research directions. Bus. Inf. Syst. Eng. 63, 441–456 (2021)

    Article  Google Scholar 

  33. Liu, B., Ding, M., Shaham, S., Rahayu, W., Farokhi, F., Lin, Z.: When machine learning meets privacy: a survey and outlook. ACM Comput. Surv. (CSUR) 54(2), 1–36 (2021)

    Article  Google Scholar 

  34. Liu, P., Xu, X., Wang, W.: Threats, attacks and defenses to federated learning: issues, taxonomy and perspectives. Cybersecurity 5(1), 1–19 (2022)

    Article  Google Scholar 

  35. Loftus, T.J., et al.: Federated learning for preserving data privacy in collaborative healthcare research. Digit. Health 8, 20552076221134456 (2022)

    Google Scholar 

  36. Ma, Z., et al.: Lightweight privacy-preserving medical diagnosis in edge computing. IEEE Trans. Serv. Comput. 15(3), 1606–1618 (2020)

    Google Scholar 

  37. McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Artificial Intelligence and Statistics, pp. 1273–1282. PMLR (2017)

    Google Scholar 

  38. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinform. 19(6), 1236–1246 (2018)

    Article  Google Scholar 

  39. Montenegro, H., Silva, W., Cardoso, J.S.: Privacy-preserving generative adversarial network for case-based explainability in medical image analysis. IEEE Access 9, 148037–148047 (2021)

    Article  Google Scholar 

  40. Nguyen, D.C., et al.: Federated learning for smart healthcare: a survey. ACM Comput. Surv. (CSUR) 55(3), 1–37 (2022)

    Article  Google Scholar 

  41. OECD: Robustness, security and safety (principle 1.4) (2023). https://oecd.ai/en/dashboards/ai-principles/P8

  42. OECD: Transparency and explainability (principle 1.3) (2023). https://oecd.ai/en/dashboards/ai-principles/P7

  43. Onesimu, J.A., Karthikeyan, J.: An efficient privacy-preserving deep learning scheme for medical image analysis. J. Inf. Technol. Manage. 12(Special Issue: The Importance of Human Computer Interaction: Challenges, Methods and Applications), 50–67 (2020)

    Google Scholar 

  44. Ouyang, D., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252–256 (2020)

    Article  Google Scholar 

  45. Park, S., Kim, G., Kim, J., Kim, B., Ye, J.C.: Federated split vision transformer for COVID-19 CXR diagnosis using task-agnostic training. arXiv preprint arXiv:2111.01338 (2021)

  46. Paul, J., Annamalai, M.S.M.S., Ming, W., Al Badawi, A., Veeravalli, B., Aung, K.M.M.: Privacy-preserving collective learning with homomorphic encryption. IEEE Access 9, 132084–132096 (2021)

    Google Scholar 

  47. Popescu, A.B., et al.: Privacy preserving classification of EEG data using machine learning and homomorphic encryption. Appl. Sci. 11(16), 7360 (2021)

    Article  Google Scholar 

  48. Qayyum, A., Qadir, J., Bilal, M., Al-Fuqaha, A.: Secure and robust machine learning for healthcare: a survey. IEEE Rev. Biomed. Eng. 14, 156–180 (2020)

    Article  Google Scholar 

  49. Ramachandram, D., Taylor, G.W.: Deep multimodal learning: a survey on recent advances and trends. IEEE Signal Process. Mag. 34(6), 96–108 (2017)

    Article  Google Scholar 

  50. Ravì, D., et al.: Deep learning for health informatics. IEEE J. Biomed. Health Inform. 21(1), 4–21 (2016)

    Article  Google Scholar 

  51. Sadilek, A., et al.: Privacy-first health research with federated learning. NPJ Digit. Med. 4(1), 132 (2021)

    Article  Google Scholar 

  52. Sarkar, E., Chielle, E., Gursoy, G., Chen, L., Gerstein, M., Maniatakos, M.: Scalable privacy-preserving cancer type prediction with homomorphic encryption. arXiv preprint arXiv:2204.05496 (2022)

  53. Shehab, M., et al.: Machine learning in medical applications: a review of state-of-the-art methods. Comput. Biol. Med. 145, 105458 (2022)

    Google Scholar 

  54. Singh, S., Shukla, K.: Privacy-preserving machine learning for medical image classification. arXiv preprint arXiv:2108.12816 (2021)

  55. Soenksen, L.R., et al.: Integrated multimodal artificial intelligence framework for healthcare applications. NPJ Digit. Med. 5(1), 149 (2022)

    Article  Google Scholar 

  56. Srivastava, S.K., Singh, S.K., Suri, J.S.: Effect of incremental feature enrichment on healthcare text classification system: a machine learning paradigm. Comput. Methods Program. Biomed. 172, 35–51 (2019)

    Article  Google Scholar 

  57. Suriyakumar, V.M., Papernot, N., Goldenberg, A., Ghassemi, M.: Chasing your long tails: differentially private prediction in health care settings. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 723–734 (2021)

    Google Scholar 

  58. T’Jonck, K., Kancharla, C.R., Pang, B., Hallez, H., Boydens, J.: Privacy preserving classification via machine learning model inference on homomorphic encrypted medical data. In: 2022 XXXI International Scientific Conference Electronics (ET), pp. 1–6. IEEE (2022)

    Google Scholar 

  59. Vaswani, A., et al.: Attention is all you need. Adv. Neural Inf. Process. Syst. 30 (2017)

    Google Scholar 

  60. Vizitiu, A., Niţă, C.I., Puiu, A., Suciu, C., Itu, L.M.: Towards privacy-preserving deep learning based medical imaging applications. In: 2019 IEEE International Symposium on Medical Measurements and Applications (MeMeA), pp. 1–6. IEEE (2020)

    Google Scholar 

  61. Voigt, P., Von dem Bussche, A.: The EU General Data Protection Regulation (GDPR). A Practical Guide, 1st edn., vol. 10, no. 3152676, pp. 10–5555. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57959-7

  62. Wang, Q., Zhou, Y.: Fedspl: federated self-paced learning for privacy-preserving disease diagnosis. Brief. Bioinform. 23(1), bbab498 (2022)

    Google Scholar 

  63. Warnat-Herresthal, S., et al.: Swarm learning as a privacy-preserving machine learning approach for disease classification. BioRxiv, pp. 2020–06 (2020)

    Google Scholar 

  64. WHO: Who issues first global report on artificial intelligence (AI) in health and six guiding principles for its design and use (2021). https://www.who.int/news/item/28-06-2021-who-issues-first-global-report-on-ai-in-health-and-six-guiding-principles-for-its-design-and-use

  65. Wibawa, F., Catak, F.O., Kuzlu, M., Sarp, S., Cali, U.: Homomorphic encryption and federated learning based privacy-preserving CNN training: COVID-19 detection use-case. In: Proceedings of the 2022 European Interdisciplinary Cybersecurity Conference, pp. 85–90 (2022)

    Google Scholar 

  66. Wiesenfeld, B.M., Aphinyanaphongs, Y., Nov, O.: AI model transferability in healthcare: a sociotechnical perspective. Nat. Mach. Intell. 4(10), 807–809 (2022)

    Article  Google Scholar 

  67. Wolff, J., et al.: Federated machine learning for a facilitated implementation of artificial intelligence in healthcare-a proof of concept study for the prediction of coronary artery calcification scores. J. Integr. Bioinform. 19(4) (2022)

    Google Scholar 

  68. Xu, J., Glicksberg, B.S., Su, C., Walker, P., Bian, J., Wang, F.: Federated learning for healthcare informatics. J. Healthc. Inform. Res. 5, 1–19 (2021)

    Article  Google Scholar 

  69. Yan, R., et al.: Label-efficient self-supervised federated learning for tackling data heterogeneity in medical imaging. IEEE Trans. Med. Imaging (2023)

    Google Scholar 

  70. Yang, J., et al.: MedMNIST v2-a large-scale lightweight benchmark for 2D and 3D biomedical image classification. Sci. Data 10(1), 41 (2023)

    Article  Google Scholar 

  71. Yang, S., et al.: Towards the practical utility of federated learning in the medical domain. arXiv preprint arXiv:2207.03075 (2022)

  72. Yue, Z., et al.: Privacy-preserving time-series medical images analysis using a hybrid deep learning framework. ACM Trans. Internet Technol. (TOIT) 21(3), 1–21 (2021)

    Article  Google Scholar 

  73. Zalonis, J., Armknecht, F., Grohmann, B., Koch, M.: Report: state of the art solutions for privacy preserving machine learning in the medical context. arXiv preprint arXiv:2201.11406 (2022)

  74. Zerka, F., et al.: Blockchain for privacy preserving and trustworthy distributed machine learning in multicentric medical imaging (C-DistriM). IEEE Access 8, 183939–183951 (2020)

    Article  Google Scholar 

  75. Zhang, L., Xu, J., Vijayakumar, P., Sharma, P.K., Ghosh, U.: Homomorphic encryption-based privacy-preserving federated learning in IoT-enabled healthcare system. IEEE Trans. Netw. Sci. Eng. (2022)

    Google Scholar 

  76. Zhang, W., Tople, S., Ohrimenko, O.: Leakage of dataset properties in multi-party machine learning. In: USENIX Security Symposium, pp. 2687–2704 (2021)

    Google Scholar 

  77. Zhang, X., Ding, J., Wu, M., Wong, S.T., Van Nguyen, H., Pan, M.: Adaptive privacy preserving deep learning algorithms for medical data. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 1169–1178 (2021)

    Google Scholar 

  78. Ziller, A., et al.: Privacy-preserving medical image analysis. arXiv preprint arXiv:2012.06354 (2020)

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Acknowledgements

This work was supported by the NYUAD Center for Interacting Urban Networks (CITIES), funded by Tamkeen under the NYUAD Research Institute Award CG001, and the Center for Cyber Security (CCS), funded by Tamkeen under NYUAD RRC Grant No. G1104.

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  1. Department of Computer Engineering, New York University Abu Dhabi, Abu Dhabi, UAE

    Alejandro Guerra-Manzanares, L. Julian Lechuga Lopez, Michail Maniatakos & Farah E. Shamout

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  1. Alejandro Guerra-Manzanares
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  2. L. Julian Lechuga Lopez
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  3. Michail Maniatakos
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Correspondence to Alejandro Guerra-Manzanares .

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  1. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Hao Chen

  2. Hong Kong University of Science and Technology, Hong Kong, Hong Kong

    Luyang Luo

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Guerra-Manzanares, A., Lopez, L.J.L., Maniatakos, M., Shamout, F.E. (2023). Privacy-Preserving Machine Learning for Healthcare: Open Challenges and Future Perspectives. In: Chen, H., Luo, L. (eds) Trustworthy Machine Learning for Healthcare. TML4H 2023. Lecture Notes in Computer Science, vol 13932. Springer, Cham. https://doi.org/10.1007/978-3-031-39539-0_3

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