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Can health entities collaboratively train deep learning models without sharing sensitive raw data? This paper proposes several configurations of a distributed deep learning method called SplitNN to facilitate such collaborations.
Navathe, Shamkant and Ceri, Stefano and Wiederhold, Gio and Dou, Jinglie, Vertical partitioning algorithms for database design, ACM Transactions on Database Systems (TODS), Vol.9(4), pp.680–710, 1984
1984
Earlier work this paper cites.
Annas, George J. , HIPAA regulations-a new era of medical-record privacy?, New England Journal of Medicine, Vol.348 (15), pp.1486–1490, 2003
2003
Earlier work this paper cites.
U.S. Centers for Disease Control and Prevention , HIPAA privacy rule and public health. Guidance from CDC and the US Department of Health and Human Services, MMWR: Morbidity and mortality weekly report, Vol.52 (1), pp.1–17, 2003
2003
Earlier work this paper cites.
Mercuri, Rebecca T. , The HIPAA-potamus in health care data security, Communications of the ACM, Vol.47 (7), pp.25–28, 2004
2004
Earlier work this paper cites.
Syverson, Paul and Dingledine, R and Mathewson, N, Tor: The second generation onion router,Usenix Security, 2004
2004
Earlier work this paper cites.
Agrawal, Sanjay and Narasayya, Vivek and Yang, Beverly, Integrating vertical and horizontal partitioning into automated physical database design, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, pp.359–370, 2004
2004
Earlier work this paper cites.
Abadi, Daniel J and Marcus, Adam and Madden, Samuel R and Hollenbach, Kate, Scalable semantic web data management using vertical partitioning, Proceedings of the 33rd international conference on Very large data bases, pp.411–422, 2007
2007
Earlier work this paper cites.
Gostin, Lawrence O., Levit, Laura A. and Nass, Sharyl J. , Beyond the HIPAA privacy rule: enhancing privacy, improving health through research, National Academies Press, 2009
2009
Earlier work this paper cites.
Luxton, David D and Kayl, Robert A and Mishkind, Matthew C. , mHealth data security: The need for HIPAA-compliant standardization, Telemedicine and e-Health, Vol.18(4), pp. 284–288, 2012
2012
Earlier work this paper cites.
Shokri, Reza and Shmatikov, Vitaly, Privacy-preserving deep learning, Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pp.1310–1321 2015
2015
Earlier work this paper cites.
Alipanahi, Babak and Delong, Andrew and Weirauch, Matthew T and Frey, Brendan J, Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning, Nature biotechnology, Vol.33(8), 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Abadi, Martin and Chu, Andy and Goodfellow, Ian and McMahan, H Brendan and Mironov, Ilya and Talwar, Kunal and Zhang, Li, Deep learning with differential privacy, Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, pp.308–318, 2016
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
McMahan, H. B. , Moore, E., Ramage, D., Hampson, S. and Aguera y Arcas, B., Communication-efficient learning of deep networks from decentralized data, 20’th International Conference on Artificial Intelligence and Statistics (AISTATS), 2017
2017
Later among the works it cites.
Ravı, Daniele and Wong, Charence and Deligianni, Fani and Berthelot, Melissa and Andreu-Perez, Javier and Lo, Benny and Yang, Guang-Zhong, Deep learning for health informatics, IEEE journal of biomedical and health informatics, Vol.21(1), pp.4–21, 2017
2017
Later among the works it cites.
Litjens, Geert and Kooi, Thijs and Bejnordi, Babak Ehteshami and Setio, Arnaud Arindra Adiyoso and Ciompi, Francesco and Ghafoorian, Mohsen and van der Laak, Jeroen AWM and Van Ginneken, Bram and Sánchez, Clara I, A survey on deep learning in medical image analysis, Medical image analysis, Vol.42, pp.60–88, 2017
2017
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Rouhani, Bita Darvish and Riazi, M Sadegh and Koushanfar, Farinaz, SecureML: A system for scalable privacy-preserving machine learning, 38th IEEE Symposium on Security and Privacy (SP), 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Bonawitz, Keith and Ivanov, Vladimir and Kreuter, Ben and Marcedone, Antonio and McMahan, H Brendan and Patel, Sarvar and Ramage, Daniel and Segal, Aaron and Seth, Karn, Practical secure aggregation for privacy-preserving machine learning, Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp.1175–1191, 2017
2017
Cited alongside, same era.
Bonawitz, Keith and Ivanov, Vladimir and Kreuter, Ben and Marcedone, Antonio and McMahan, H Brendan and Patel, Sarvar and Ramage, Daniel and Segal, Aaron and Seth, Karn, Practical secure aggregation for privacy-preserving machine learning, Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security, pp.1175–1191, 2017
2017
Cited alongside, same era.
Miotto, Riccardo and Wang, Fei and Wang, Shuang and Jiang, Xiaoqian and Dudley, Joel T., Deep learning for healthcare: review, opportunities and challenges, Briefings in bioinformatics, 2017
2017
Cited alongside, same era.
2017
Later among the works it cites.
Louizos, Christos and Ullrich, Karen and Welling, Max, Bayesian compression for deep learning, Advances in Neural Information Processing Systems, pp.3288–3298, 2017
2017
Later among the works it cites.
Swedish, T. and Raskar, R., Deep Visual Teach and Repeat on Path Networks, The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2018
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
2018
Closest in time.
Shickel, Benjamin and Tighe, Patrick James and Bihorac, Azra and Rashidi, Parisa, Deep EHR: A survey of recent advances in deep learning techniques for electronic health record (EHR) analysis, IEEE journal of biomedical and health informatics, Vol.22(5) pp.1589–1604, 2018
2018
Closest in time.
Ching, Travers and Himmelstein, Daniel S and Beaulieu-Jones, Brett K and Kalinin, Alexandr A and Do, Brian T and Way, Gregory P and Ferrero, Enrico and Agapow, Paul-Michael and Zietz, Michael and Hoffman, Michael M , Opportunities and obstacles for deep learning in biology and medicine, Journal of The Royal Society Interface, Vol.15(141), 2018
2018
Closest in time.
Gupta, Otkrist and Raskar, Ramesh, Distributed learning of deep neural network over multiple agents, Journal of Network and Computer Applications, Vol.116, pp.1–8, 2018
2018
Closest in time.