{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,24]],"date-time":"2026-03-24T01:14:27Z","timestamp":1774314867134,"version":"3.50.1"},"publisher-location":"Cham","reference-count":27,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031185229","type":"print"},{"value":"9783031185236","type":"electronic"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-18523-6_9","type":"book-chapter","created":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T10:04:48Z","timestamp":1665223488000},"page":"89-99","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Content-Aware Differential Privacy with\u00a0Conditional Invertible Neural Networks"],"prefix":"10.1007","author":[{"given":"Malte","family":"T\u00f6lle","sequence":"first","affiliation":[]},{"given":"Ullrich","family":"K\u00f6the","sequence":"additional","affiliation":[]},{"given":"Florian","family":"Andr\u00e9","sequence":"additional","affiliation":[]},{"given":"Benjamin","family":"Meder","sequence":"additional","affiliation":[]},{"given":"Sandy","family":"Engelhardt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,7]]},"reference":[{"key":"9_CR1","doi-asserted-by":"publisher","unstructured":"Abadi, M., et al.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016). https:\/\/doi.org\/10.1145\/2976749.2978318","DOI":"10.1145\/2976749.2978318"},{"key":"9_CR2","unstructured":"Ardizzone, L., Kruse, J., Rother, C., K\u00f6the, U.: Analyzing inverse problems with invertible neural networks. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=rJed6j0cKX"},{"key":"9_CR3","doi-asserted-by":"crossref","unstructured":"Ardizzone, L., L\u00fcth, C., Kruse, J., Rother, C., K\u00f6the, U.: Conditional invertible neural networks for guided image generation (2020). https:\/\/openreview.net\/forum?id=SyxC9TEtPH","DOI":"10.1007\/978-3-030-71278-5_27"},{"key":"9_CR4","doi-asserted-by":"crossref","unstructured":"Bellovin, S., Dutta, P., Reitlinger, N.: Privacy and synthetic datasets. Stan. Technol. Law Rev. (2018)","DOI":"10.31228\/osf.io\/bfqh3"},{"key":"9_CR5","doi-asserted-by":"publisher","first-page":"3249","DOI":"10.1109\/TMI.2021.3077857","volume":"40","author":"S Bhadra","year":"2021","unstructured":"Bhadra, S., Kelkar, V.A., Brooks, F.J., Anastasio, M.A.: On hallucinations in tomographic image reconstruction. IEEE Trans. Med. Imaging 40, 3249\u20133260 (2021)","journal-title":"IEEE Trans. Med. Imaging"},{"key":"9_CR6","doi-asserted-by":"crossref","unstructured":"Bissoto, A., Perez, F., Valle, E., Avila, S.: Skin lesion synthesis with generative adversarial networks. In: OR 2.0 Context-Aware Operating Theaters, Computer Assisted Robotic Endoscopy, Clinical Image-Based Procedures, and Skin Image Analysis, pp. 294\u2013302 (2018)","DOI":"10.1007\/978-3-030-01201-4_32"},{"key":"9_CR7","unstructured":"Dinh, L., Krueger, D., Bengio, Y.: Nice: non-linear independent components estimation. In: International Conference on Learning Representations (2015)"},{"key":"9_CR8","unstructured":"Dinh, L., Sohl-Dickstein, J., Bengio, S.: Density estimation using real NVP. In: International Conference on Learning Representations (2017). https:\/\/openreview.net\/forum?id=HkpbnH9lx"},{"key":"9_CR9","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93030-0","volume":"11","author":"C Dwork","year":"2021","unstructured":"Dwork, C., Roth, A.: Medical imaging deep learning with differential privacy. Sci. Rep. 11, 1\u20138 (2021). https:\/\/doi.org\/10.1038\/s41598-021-93030-0","journal-title":"Sci. Rep."},{"key":"9_CR10","doi-asserted-by":"crossref","unstructured":"Fan, L.: Image pixelization with differential privacy. In: DBSec (2018)","DOI":"10.1007\/978-3-319-95729-6_10"},{"key":"9_CR11","doi-asserted-by":"publisher","unstructured":"Frome, A., et al.: Large-scale privacy protection in google street view. In: International Conference on Computer Vision, pp. 2373\u20132380 (2009). https:\/\/doi.org\/10.1109\/ICCV.2009.5459413","DOI":"10.1109\/ICCV.2009.5459413"},{"key":"9_CR12","doi-asserted-by":"publisher","unstructured":"Kermany, D., Zhang, K., Goldbaum, M.: Large dataset of labeled optical coherence tomography (OCT) and chest X-ray images. Cell (2018). https:\/\/doi.org\/10.17632\/rscbjbr9sj.3","DOI":"10.17632\/rscbjbr9sj.3"},{"key":"9_CR13","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: International Conference of Learning Representations (2015)"},{"key":"9_CR14","unstructured":"Kingma, D.P., Dhariwal, P.: Glow: generative flow with invertible $$1\\times 1$$ convolutions. In: Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"9_CR15","doi-asserted-by":"crossref","unstructured":"Laves, M.H., T\u00f6lle, M., Ortmaier, T.: Uncertainty estimation in medical image denoising with Bayesian deep image prior. In: Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Graphs in Biomedical Image Analysis, pp. 81\u201396 (2020)","DOI":"10.1007\/978-3-030-60365-6_9"},{"key":"9_CR16","unstructured":"LeCun, Y., Cortes, C., Burges, C.: MNIST handwritten digit database. ATT Labs, vol. 2 (2010). https:\/\/yann.lecun.com\/exdb\/mnist"},{"key":"9_CR17","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: International Conference on Computer Vision (ICCV), December 2015","DOI":"10.1109\/ICCV.2015.425"},{"key":"9_CR18","unstructured":"McPherson, R., Shokri, R., Shmatikov, V.: Defeating image obfuscation with deep learning (2016)"},{"key":"9_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1007\/978-3-319-46487-9_2","volume-title":"Computer Vision \u2013 ECCV 2016","author":"SJ Oh","year":"2016","unstructured":"Oh, S.J., Benenson, R., Fritz, M., Schiele, B.: Faceless person recognition: privacy implications in social media. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 19\u201335. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46487-9_2"},{"key":"9_CR20","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825\u20132830 (2011)","journal-title":"J. Mach. Learn. Res."},{"key":"9_CR21","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-021-00507-3","volume":"4","author":"AD Sch\u00fctte","year":"2021","unstructured":"Sch\u00fctte, A.D., et al.: Overcoming barriers to data sharing with medical image generation: a comprehensive evaluation. NPJ Digit. Med. 4, 1\u201314 (2021). https:\/\/doi.org\/10.1038\/s41746-021-00507-3","journal-title":"NPJ Digit. Med."},{"key":"9_CR22","unstructured":"Sorrenson, P., Rother, C., K\u00f6the, U.: Disentanglement by nonlinear ICA with general incompressible-flow networks (GIN). In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=rygeHgSFDH"},{"issue":"9","key":"9_CR23","doi-asserted-by":"publisher","first-page":"749","DOI":"10.1038\/s42256-021-00390-3","volume":"3","author":"D Usynin","year":"2021","unstructured":"Usynin, D., et al.: Adversarial interference and its mitigations in privacy-preserving collaborative machine learning. Nat. Mach. Intell. 3(9), 749\u2013758 (2021). https:\/\/doi.org\/10.1038\/s42256-021-00390-3","journal-title":"Nat. Mach. Intell."},{"key":"9_CR24","doi-asserted-by":"crossref","unstructured":"Waites, C., Cummings, R.: Differentially private normalizing flows for privacy-preserving density estimation. In: AAAI\/ACM Conference on AI, Ethics, and Society (2021)","DOI":"10.1145\/3461702.3462625"},{"key":"9_CR25","unstructured":"Yoon, J., Jordon, J., van der Schaar, M.: PATE-GAN: generating synthetic data with differential privacy guarantees. In: International Conference on Learning Representations (2019). https:\/\/openreview.net\/forum?id=S1zk9iRqF7"},{"key":"9_CR26","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1561\/0400000042","volume":"9","author":"A Ziller","year":"2014","unstructured":"Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., Kaissis, G.: The algorithmic foundations of differential privacy. Found. Trends Theor. Comput. Sci. 9, 211\u2013407 (2014). https:\/\/doi.org\/10.1561\/0400000042","journal-title":"Found. Trends Theor. Comput. Sci."},{"issue":"1","key":"9_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-021-93030-0","volume":"11","author":"A Ziller","year":"2021","unstructured":"Ziller, A., Usynin, D., Braren, R., Makowski, M., Rueckert, D., Kaissis, G.: Medical imaging deep learning with differential privacy. Sci. Rep. 11(1), 1\u20138 (2021). https:\/\/doi.org\/10.1038\/s41598-021-93030-0","journal-title":"Sci. Rep."}],"container-title":["Lecture Notes in Computer Science","Distributed, Collaborative, and Federated Learning, and Affordable AI and Healthcare for Resource Diverse Global Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-18523-6_9","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,10,8]],"date-time":"2022-10-08T10:06:09Z","timestamp":1665223569000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-18523-6_9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031185229","9783031185236"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-18523-6_9","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"7 October 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DeCaF","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Distributed, Collaborative, and Federated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"decaf2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/decaf-workshop.github.io\/decaf-2022\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"14","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}