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Privacy Issues in Magnetic Resonance Images

Year 2023, Volume: 12 Issue: 1, 21 - 31, 10.03.2023
https://doi.org/10.55859/ijiss.1212964

Abstract

Privacy in magnetic resonance imaging (MRI) plays an important role due to violations occurring in scanning, storing, transferring, analyzing, and sharing. This paper reviews privacy concerns in MRI and especially Brain MRI in terms of datasets, models, platforms, violations, solutions used in privacy and security in the literature, discusses important issues based on risks, techniques, policies, rules, and existing and missing points in MRIs. Even if there have been rules, regulations, policies, and laws available for preserving privacy with the available techniques anonymization, differential privacy, federated learning, pseudonymization, synthetic data generation, privacy-utility or anonymization-utility dilemma is still on novel privacy-enhancing, or preserving techniques are always required to handle sensitive data with care. This paper focuses on these issues with some suggestions, and also discusses these issues for future directions.

References

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  • H. A. Al Hamid, Sk. Md. M. Rahman, M. S. Hossain, A. Almogren, and A. Alamri, “A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography,” IEEE Access, vol. 5, pp. 22313–22328, 2017. 
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  • J. Luo and S. Wu, "Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification," 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022. 
  • M. H. Wu, J. Zhao, B. Chen, Y. Zhang, Y. Yu and J. Cheng, “Reversible data hiding based on medical image systems by means of histogram strategy,” 2018 3rd International Conference on Information Systems Engineering (ICISE), 2018.
  • H. Imtiaz, J. Mohammadi, R. Silva, B. Baker, S. M. Plis, A. D. Sarwate, and V. D. Calhoun, “A correlated noise-assisted decentralized differentially private estimation protocol, and its application to fmri source separation,” IEEE Transactions on Signal Processing, vol. 69, pp. 6355–6370, 2021. 
  • L. Lindner, D. Narnhofer, M. Weber, C. Gsaxner, M. Kolodziej, and J. Egger, “Using synthetic training data for Deep Learning-based GBM segmentation,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. 
  • G.P. Diller, J. Vahle, R. Radke, M. L. B. Vidal, A. J. Fischer, U. M. M. Bauer, S. Sarikouch, F. Berger, P. Beerbaum, H. Baumgartner, and S. Orwat, “Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease,” BMC Medical Imaging, vol. 20, no. 1, 2020.
  • L. Xiang, “Survey on privacy preserving techniques for publishing social network data,” Journal of Software, vol. 25, pp. 576–590, 2014. 
  • European Society of Radiology (ESR), “The new EU General Data Protection Regulation: What the radiologist should know,” Insights into Imaging, vol. 8, no. 3, pp. 295–299, 2017. 
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  • Information Commissioner’s Office (ICO), “Security outcomes”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/security/security-outcomes/}. 
  • S. M. Mousavi, A. Naghsh, A. A. Manaf and S. A. R. Abu-Bakar, “A robust medical image watermarking against salt and pepper noise for Brain MRI images,” Multimedia Tools and Applications, vol. 76, no. 7, pp. 10313–10342, 2016. 
  • R. Tamilselvi, A. Nagaraj, M. P. Beham, and M. B. Sandhiya, “Bramsit: A database for brain tumor diagnosis and detection,” 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), 2020. 
  • A. Sayah, C. Bencheqroun , K. Bhuvaneshwar, A. Belouali, S. Bakas, C. Sako, C. Davatzikos, A. Alaoui, S. Madhavan, and Y. Gusev, “Enhancing the Rembrandt MRI collection with expert segmentation labels and Quantitative Radiomic features,” Scientific Data, vol. 9, no. 1, 2022.
  • J. Shapey, A. Kujawa, R. Dorent, G. Wang, A. Dimitriadis, D. Grishchuk, I. Paddick, N. Kitchen, R. Bradford, S. R. Saeed, S. Bisdas, S. Ourselin, and T. Vercauteren, “Segmentation of vestibular schwannoma from MRI, an open annotated dataset and Baseline Algorithm,” Scientific Data, vol. 8, no. 1, 2021. 
  • L. Snoek, M. M. van der Miesen, T. Beemsterboer, A. van der Leij, A. Eigenhuis, and H. S. Scholte, “The Amsterdam open MRI collection, a set of multimodal MRI datasets for individual difference analyses,” Scientific Data, vol. 8, no. 1, 2021. 
  • S. L. Liew, B. P. Lo, M. R. Donnelly, A. Zavaliangos-Petropulu, J. N. Jeong, G. Barisano, A. Hutton, J. P. Simon, J. M. Juliano, A. Suri, Z. Wang, A. Abdullah, J. Kim, T. Ard, N. Banaj, M. R. Borich, L. A. Boyd, A. Brodtmann, C. M. Buetefisch, L. Cao, J. M. Cassidy, V. Ciullo, A. B. Conforto, S. C. Cramer, R. Dacosta-Aguayo, E. de la Rosa, M. Domin, A. N. Dula, W. Feng, A. R. Franco, F. Geranmayeh, A. Gramfort, C. M. Gregory, S. A. Hanlon, S. G. Hordacre, S. A. Kautz, M. S. Khlif, H. Kim, J. S. Kirschke, J. Liu, M. Lotze, B. J. MacIntosh, M. Mataró, F. B. Mohamed, J. E. Nordvik, G. Park, A. Pienta, F. Piras, S. M. Redman, K. P. Revill, M. Reyes, A. D. Robertson, N. J. Seo, S. R. Soekadar, G. Spalletta, A. Sweet, M. Telenczuk, G. Thielman, L. T. Westlye, C. J. Winstein, G. F. Wittenberg, K. A. Wong, and C. Yu, “A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms,” Scientific Data, vol. 9, no. 1, 2022. 
  • S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing the cancer genome Atlas Glioma MRI collections with expert segmentation labels and Radiomic features,” Scientific Data, vol. 4, no. 1, 2017.
  • Digital Transformation Office of the Presidency of Turkey, “Turk Brain Project”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://cbddo.gov.tr/en/projects/turkish-brain-project/}.    
  • European Parliament “Official Legal Text,” General Data Protection Regulation (GDPR), Accessed Aug. 14, 2022. [Online]. Available: \url{https://gdpr-info.eu/}. 
  • M. R. Cohen, “A legal guide to Privacy and Data Security”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://www.leg.mn.gov/docs/2019/other/190030.pdf}. 
  • A. E. Cetinkaya, M. Akin, and S. Sagiroglu, “A communication efficient federated learning approach to Multi Chest Diseases Classification,” 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 429-434, 15-17 Sep 2021.  
  • T. Mahler, Tom Mahler, N. Nissim, E. Shalom, I. Goldenberg, G. Hassman, A. Makori, I. Kochav, Y. Elovici, and Y. Shahar, “Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices”, 2018.   
  • Rebekah Moan, “Report: 45M medical images are accessible online”, Accessed Aug. 29, 2022. [Online]. Available: \url{https://www.auntminnie.com/index.aspx?sec=sup&sub=pac&pag=dis&ItemID=131133}.  
  • W. N. Price and I. G. Cohen, “Privacy in the age of Medical Big Data,” Nature Medicine, vol. 25, no. 1, pp. 37–43, 2019.  
  • A. Shahid, M. H. Bazargani, P. Banahan, B. M. Namee, T. Kechadi, C. Treacy, G. Regan, and P. MacMahon, “A two-stage de-identification process for privacy-preserving medical image analysis,” Healthcare, vol. 10, no. 5, p. 755, 2022.  
  • M. Elhoseny, N. N. Thilakarathne, M. I. Alghamdi, R. K. Mahendran, A. A. Gardezi, H. Weerasinghe, and A. Welhenge, “Security and privacy issues in medical internet of things: Overview, countermeasures, challenges and future directions,” Sustainability, vol. 13, no. 21, p. 11645, 2021.  
  • T. White, E. Blok, and V. D. Calhoun, “Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed,” Human Brain Mapping, vol. 43, no. 1, pp. 278–291, 2020.  
  • M. Bezzi and J.-C. Pazzaglia, “The anonymity vs. utility dilemma,” ISSE 2008 Securing Electronic Business Processes, pp. 99–107, 2009.
Year 2023, Volume: 12 Issue: 1, 21 - 31, 10.03.2023
https://doi.org/10.55859/ijiss.1212964

Abstract

References

  • The U.S. Department of Health and Human Services (HSS), “Methods for de-identification of PHI”, Accessed Aug. 14, 2022 [Online]. Available: \url{https://www.hhs.gov/hipaa/for-professionals/privacy/special-topics/de-identification/index.html}. 
  • A. Majeed, “Attribute-centric anonymization scheme for improving user privacy and utility of publishing e-health Data,” Journal of King Saud University - Computer and Information Sciences, vol. 31, no. 4, pp. 426–435, 2019. 
  • E. Mikulan, S. Russo, F. M. Zauli, P. d’Orio, S. Parmigiani, J. Favaro, W. Knight, S. Squarza, P. Perri, F. Cardinale, P. Avanzini and A. Pigorini, “A comparative study between state‐of‐the‐art mri de-identification and anonyMI, a new method combining re‐identification risk reduction and geometrical preservation,” Human Brain Mapping, vol.42, no.17, pp.5523–5534, 2021. 
  • A. de Sitter, M. Visser, I. Brouwer, K. S. Cover, R. A. van Schijndel, R. S. Eijgelaar, D. M. J. Müller, S. Ropele, L. Kappos, Á. Rovira, M. Filippi, C. Enzinger, J. Frederiksen, O. Ciccarelli, C. R. G. Guttmann, M. P. Wattjes, M. G. Witte, P. C. de Witt Hamer , F. Barkhof and H. Vrenken, “Facing privacy in neuroimaging: Removing facial features degrades performance of image analysis methods,” European Radiology, vol. 30, no. 2, pp. 1062–1074, 2019.   
  • J. Vincent, W. Pan, and G. Coatrieux, “Privacy protection and security in eHealth Cloud Platform for medical image sharing,” 2016 2nd International Conference on Advanced Technologies for Signal and Image Processing (ATSIP), 2016. 
  • S. Khalifeh, J. Georgi, and S. Shakhatreh, “Design and implementation of a steganography-based system that provides protection for breast cancer patient's data,” 2022 56th Annual Conference on Information Sciences and Systems (CISS), 2022. 
  • H. A. Al Hamid, Sk. Md. M. Rahman, M. S. Hossain, A. Almogren, and A. Alamri, “A security model for preserving the privacy of medical big data in a healthcare cloud using a fog computing facility with pairing-based cryptography,” IEEE Access, vol. 5, pp. 22313–22328, 2017. 
  • S. M. Mousavi, A. Naghsh, and S. A. Abu-Bakar, “Watermarking techniques used in medical images: A survey,” Journal of Digital Imaging, vol. 27, no. 6, pp. 714–729, 2014. 
  • J. Luo and S. Wu, "Fedsld: Federated Learning with Shared Label Distribution for Medical Image Classification," 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 2022. 
  • M. H. Wu, J. Zhao, B. Chen, Y. Zhang, Y. Yu and J. Cheng, “Reversible data hiding based on medical image systems by means of histogram strategy,” 2018 3rd International Conference on Information Systems Engineering (ICISE), 2018.
  • H. Imtiaz, J. Mohammadi, R. Silva, B. Baker, S. M. Plis, A. D. Sarwate, and V. D. Calhoun, “A correlated noise-assisted decentralized differentially private estimation protocol, and its application to fmri source separation,” IEEE Transactions on Signal Processing, vol. 69, pp. 6355–6370, 2021. 
  • L. Lindner, D. Narnhofer, M. Weber, C. Gsaxner, M. Kolodziej, and J. Egger, “Using synthetic training data for Deep Learning-based GBM segmentation,” 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2019. 
  • G.P. Diller, J. Vahle, R. Radke, M. L. B. Vidal, A. J. Fischer, U. M. M. Bauer, S. Sarikouch, F. Berger, P. Beerbaum, H. Baumgartner, and S. Orwat, “Utility of deep learning networks for the generation of artificial cardiac magnetic resonance images in congenital heart disease,” BMC Medical Imaging, vol. 20, no. 1, 2020.
  • L. Xiang, “Survey on privacy preserving techniques for publishing social network data,” Journal of Software, vol. 25, pp. 576–590, 2014. 
  • European Society of Radiology (ESR), “The new EU General Data Protection Regulation: What the radiologist should know,” Insights into Imaging, vol. 8, no. 3, pp. 295–299, 2017. 
  • Information Commissioner’s Office (ICO), “Principle (e): Storage limitation”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/principles/storage-limitation}. 
  • Information Commissioner’s Office (ICO), “Principle (b): Purpose limitation”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/principles/purpose-limitation/}. 
  • Information Commissioner’s Office (ICO), “Security outcomes”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://ico.org.uk/for-organisations/guide-to-data-protection/guide-to-the-general-data-protection-regulation-gdpr/security/security-outcomes/}. 
  • S. M. Mousavi, A. Naghsh, A. A. Manaf and S. A. R. Abu-Bakar, “A robust medical image watermarking against salt and pepper noise for Brain MRI images,” Multimedia Tools and Applications, vol. 76, no. 7, pp. 10313–10342, 2016. 
  • R. Tamilselvi, A. Nagaraj, M. P. Beham, and M. B. Sandhiya, “Bramsit: A database for brain tumor diagnosis and detection,” 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII), 2020. 
  • A. Sayah, C. Bencheqroun , K. Bhuvaneshwar, A. Belouali, S. Bakas, C. Sako, C. Davatzikos, A. Alaoui, S. Madhavan, and Y. Gusev, “Enhancing the Rembrandt MRI collection with expert segmentation labels and Quantitative Radiomic features,” Scientific Data, vol. 9, no. 1, 2022.
  • J. Shapey, A. Kujawa, R. Dorent, G. Wang, A. Dimitriadis, D. Grishchuk, I. Paddick, N. Kitchen, R. Bradford, S. R. Saeed, S. Bisdas, S. Ourselin, and T. Vercauteren, “Segmentation of vestibular schwannoma from MRI, an open annotated dataset and Baseline Algorithm,” Scientific Data, vol. 8, no. 1, 2021. 
  • L. Snoek, M. M. van der Miesen, T. Beemsterboer, A. van der Leij, A. Eigenhuis, and H. S. Scholte, “The Amsterdam open MRI collection, a set of multimodal MRI datasets for individual difference analyses,” Scientific Data, vol. 8, no. 1, 2021. 
  • S. L. Liew, B. P. Lo, M. R. Donnelly, A. Zavaliangos-Petropulu, J. N. Jeong, G. Barisano, A. Hutton, J. P. Simon, J. M. Juliano, A. Suri, Z. Wang, A. Abdullah, J. Kim, T. Ard, N. Banaj, M. R. Borich, L. A. Boyd, A. Brodtmann, C. M. Buetefisch, L. Cao, J. M. Cassidy, V. Ciullo, A. B. Conforto, S. C. Cramer, R. Dacosta-Aguayo, E. de la Rosa, M. Domin, A. N. Dula, W. Feng, A. R. Franco, F. Geranmayeh, A. Gramfort, C. M. Gregory, S. A. Hanlon, S. G. Hordacre, S. A. Kautz, M. S. Khlif, H. Kim, J. S. Kirschke, J. Liu, M. Lotze, B. J. MacIntosh, M. Mataró, F. B. Mohamed, J. E. Nordvik, G. Park, A. Pienta, F. Piras, S. M. Redman, K. P. Revill, M. Reyes, A. D. Robertson, N. J. Seo, S. R. Soekadar, G. Spalletta, A. Sweet, M. Telenczuk, G. Thielman, L. T. Westlye, C. J. Winstein, G. F. Wittenberg, K. A. Wong, and C. Yu, “A large, curated, open-source stroke neuroimaging dataset to improve lesion segmentation algorithms,” Scientific Data, vol. 9, no. 1, 2022. 
  • S. Bakas, H. Akbari, A. Sotiras, M. Bilello, M. Rozycki, J. S. Kirby, J. B. Freymann, K. Farahani, and C. Davatzikos, “Advancing the cancer genome Atlas Glioma MRI collections with expert segmentation labels and Radiomic features,” Scientific Data, vol. 4, no. 1, 2017.
  • Digital Transformation Office of the Presidency of Turkey, “Turk Brain Project”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://cbddo.gov.tr/en/projects/turkish-brain-project/}.    
  • European Parliament “Official Legal Text,” General Data Protection Regulation (GDPR), Accessed Aug. 14, 2022. [Online]. Available: \url{https://gdpr-info.eu/}. 
  • M. R. Cohen, “A legal guide to Privacy and Data Security”, Accessed Aug. 14, 2022. [Online]. Available: \url{https://www.leg.mn.gov/docs/2019/other/190030.pdf}. 
  • A. E. Cetinkaya, M. Akin, and S. Sagiroglu, “A communication efficient federated learning approach to Multi Chest Diseases Classification,” 2021 6th International Conference on Computer Science and Engineering (UBMK), pp. 429-434, 15-17 Sep 2021.  
  • T. Mahler, Tom Mahler, N. Nissim, E. Shalom, I. Goldenberg, G. Hassman, A. Makori, I. Kochav, Y. Elovici, and Y. Shahar, “Know Your Enemy: Characteristics of Cyber-Attacks on Medical Imaging Devices”, 2018.   
  • Rebekah Moan, “Report: 45M medical images are accessible online”, Accessed Aug. 29, 2022. [Online]. Available: \url{https://www.auntminnie.com/index.aspx?sec=sup&sub=pac&pag=dis&ItemID=131133}.  
  • W. N. Price and I. G. Cohen, “Privacy in the age of Medical Big Data,” Nature Medicine, vol. 25, no. 1, pp. 37–43, 2019.  
  • A. Shahid, M. H. Bazargani, P. Banahan, B. M. Namee, T. Kechadi, C. Treacy, G. Regan, and P. MacMahon, “A two-stage de-identification process for privacy-preserving medical image analysis,” Healthcare, vol. 10, no. 5, p. 755, 2022.  
  • M. Elhoseny, N. N. Thilakarathne, M. I. Alghamdi, R. K. Mahendran, A. A. Gardezi, H. Weerasinghe, and A. Welhenge, “Security and privacy issues in medical internet of things: Overview, countermeasures, challenges and future directions,” Sustainability, vol. 13, no. 21, p. 11645, 2021.  
  • T. White, E. Blok, and V. D. Calhoun, “Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed,” Human Brain Mapping, vol. 43, no. 1, pp. 278–291, 2020.  
  • M. Bezzi and J.-C. Pazzaglia, “The anonymity vs. utility dilemma,” ISSE 2008 Securing Electronic Business Processes, pp. 99–107, 2009.
There are 36 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Mahmut Kapkiç 0000-0001-5065-529X

Şeref Sağıroğlu 0000-0003-0805-5818

Publication Date March 10, 2023
Submission Date December 1, 2022
Published in Issue Year 2023 Volume: 12 Issue: 1

Cite

IEEE M. Kapkiç and Ş. Sağıroğlu, “Privacy Issues in Magnetic Resonance Images”, IJISS, vol. 12, no. 1, pp. 21–31, 2023, doi: 10.55859/ijiss.1212964.