[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content

A Summary and Personal Perspective on Recent Advances in Privacy Risk Assessment in Digital Pathology Through Formal Methods

  • Chapter
  • First Online:
Taming the Infinities of Concurrency

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14660))

  • 227 Accesses

Abstract

This paper summarizes a recently published approach to assessing privacy risks in sharing whole-slide images. The particular focus is on aspects related to the novel application of formal methods to evaluate possible privacy breaches due to the unrestricted sharing of microscopic tissue images. This paper also briefly describes the process of creating such a model and the obstacles a theoretical computer scientist must overcome to apply formal methods in medicine successfully.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
GBP 19.95
Price includes VAT (United Kingdom)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
GBP 89.99
Price includes VAT (United Kingdom)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
GBP 109.99
Price includes VAT (United Kingdom)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. El Emam, K.: Risk-based de-identification of health data. IEEE Secur. Priv. 8(3), 64–67 (2010)

    Article  Google Scholar 

  2. El Emam, K.: Guide to the De-identification of Personal Health Information. CRC Press (2013)

    Google Scholar 

  3. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778 (2015)

    Google Scholar 

  4. Holub, P., et al.: Privacy risks of whole-slide image sharing in digital pathology. Nat. Commun. 14, 2577 (2023)

    Article  Google Scholar 

  5. Holzinger, A., et al.: Machine learning and knowledge extraction in digital pathology needs an integrative approach. In: BIRS-IMLKE (2015)

    Google Scholar 

  6. Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z.B., Swami, A.: The limitations of deep learning in adversarial settings. In: 2016 IEEE European Symposium on Security and Privacy (EuroS&P), pp. 372–387. IEEE (2016)

    Google Scholar 

  7. Plass, M., et al.: Provenance of specimen and data - a prerequisite for AI development in computational pathology. New Biotechnol. 78, 22–28 (2023)

    Article  Google Scholar 

  8. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR (2015)

    Google Scholar 

  9. Wittner, R., et al.: Lightweight distributed provenance model for complex real-world environments. Sci. Data 9(1), 503 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Tomáš Brázdil .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Brázdil, T. (2024). A Summary and Personal Perspective on Recent Advances in Privacy Risk Assessment in Digital Pathology Through Formal Methods. In: Kiefer, S., Křetínský, J., Kučera, A. (eds) Taming the Infinities of Concurrency. Lecture Notes in Computer Science, vol 14660. Springer, Cham. https://doi.org/10.1007/978-3-031-56222-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-56222-8_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56221-1

  • Online ISBN: 978-3-031-56222-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics