{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T10:56:06Z","timestamp":1768042566529,"version":"3.49.0"},"reference-count":76,"publisher":"Springer Science and Business Media LLC","issue":"9-10","license":[{"start":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T00:00:00Z","timestamp":1499904000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T00:00:00Z","timestamp":1499904000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R01GM103309"],"award-info":[{"award-number":["R01GM103309"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1502273"],"award-info":[{"award-number":["1502273"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1523115"],"award-info":[{"award-number":["1523115"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["1118050"],"award-info":[{"award-number":["1118050"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mach Learn"],"published-print":{"date-parts":[[2017,10]]},"DOI":"10.1007\/s10994-017-5656-2","type":"journal-article","created":{"date-parts":[[2017,7,13]],"date-time":"2017-07-13T18:50:19Z","timestamp":1499971819000},"page":"1681-1704","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":69,"title":["Preserving differential privacy in convolutional deep belief networks"],"prefix":"10.1007","volume":"106","author":[{"given":"NhatHai","family":"Phan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xintao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dejing","family":"Dou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2017,7,13]]},"reference":[{"key":"5656_CR1","unstructured":"Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., & Zhang, L. (2016). Deep learning with differential privacy. arXiv:1607.00133 ."},{"key":"5656_CR2","volume-title":"Mathematical methods for physicists","author":"G Arfken","year":"1985","unstructured":"Arfken, G. (1985). Mathematical methods for physicists (3rd ed.). Cambridge: Academic Press.","edition":"3"},{"issue":"21","key":"5656_CR3","doi-asserted-by":"publisher","first-page":"1082","DOI":"10.1049\/el.2009.1704","volume":"45","author":"A Armato","year":"2009","unstructured":"Armato, A., Fanucci, L., Pioggia, G., & Rossi, D. D. (2009). Low-error approximation of artificial neuron sigmoid function and its derivative. Electronics Letters, 45(21), 1082\u20131084.","journal-title":"Electronics Letters"},{"issue":"7","key":"5656_CR4","doi-asserted-by":"publisher","first-page":"e0130,140","DOI":"10.1371\/journal.pone.0130140","volume":"10","author":"S Bach","year":"2015","unstructured":"Bach, S., Binder, A., Montavon, G., Klauschen, F., M\u00fcller, K. R., & Samek, W. (2015). On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE, 10(7), e0130,140.","journal-title":"PLoS ONE"},{"issue":"9","key":"5656_CR5","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1037\/0003-066X.44.9.1175","volume":"44","author":"A Bandura","year":"1989","unstructured":"Bandura, A. (1989). Human agency in social cognitive theory. The American Psychologist, 44(9), 1175.","journal-title":"The American Psychologist"},{"issue":"1","key":"5656_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/2200000006","volume":"2","author":"Y Bengio","year":"2009","unstructured":"Bengio, Y. (2009). Learning deep architectures for AI. Foundation and Trends in Machine Learning, 2(1), 1\u2013127. doi: 10.1561\/2200000006 .","journal-title":"Foundation and Trends in Machine Learning"},{"key":"5656_CR7","unstructured":"Bengio, Y. (2017). Is cross-validation heavily used in deep learning or is it too expensive to be used? Quora. https:\/\/wwwquoracom\/Is-cross-validation-heavily-used-in-Deep-Learning-or-is-it-too-expensive-to-be-used."},{"key":"5656_CR8","doi-asserted-by":"crossref","unstructured":"Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H., Montreal, U. D., & Quebec, M. (2007). Greedy layer-wise training of deep networks. In NIPS.","DOI":"10.7551\/mitpress\/7503.003.0024"},{"key":"5656_CR9","unstructured":"Brownlee, J. (2015). 8 tactics to combat imbalanced classes in your machine learning dataset. http:\/\/machinelearningmastery.com\/tactics-to-combat-imbalanced-classes-in-your-machine-learning-dataset\/ ."},{"key":"5656_CR10","doi-asserted-by":"crossref","unstructured":"Chan, T. H. H., Li, M., Shi, E., & Xu, W. (2012). Differentially private continual monitoring of heavy hitters from distributed streams. In PETS\u201912 (pp. 140\u2013159).","DOI":"10.1007\/978-3-642-31680-7_8"},{"key":"5656_CR11","unstructured":"Chaudhuri, K., & Monteleoni, C. (2008a). Privacy-preserving logistic regression. In NIPS (pp. 289\u2013296)."},{"key":"5656_CR12","unstructured":"Chaudhuri, K., & Monteleoni, C. (2008b). Privacy-preserving logistic regression. In NIPS\u201908 (pp. 289\u2013296)."},{"key":"5656_CR13","doi-asserted-by":"crossref","unstructured":"Cheng, Y., Wang, F., Zhang, P., & Hu, J. (2016). Risk prediction with electronic health records: A deep learning approach. In SDM\u201916.","DOI":"10.1137\/1.9781611974348.49"},{"key":"5656_CR14","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocw112","author":"E Choi","year":"2016","unstructured":"Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2016). Using recurrent neural network models for early detection of heart failure onset. Journal of the American Medical Informatics Association,. doi: 10.1093\/jamia\/ocw112 .","journal-title":"Journal of the American Medical Informatics Association"},{"key":"5656_CR15","doi-asserted-by":"crossref","unstructured":"Cormode, G. (2011). Personal privacy vs population privacy: Learning to attack anonymization. In KDD\u201911 (pp. 1253\u20131261).","DOI":"10.1145\/2020408.2020598"},{"key":"5656_CR16","unstructured":"Dowlin, N., Gilad-Bachrach, R., Laine, K., Lauter, K., Naehrig, M., & Wernsing, J. (2016). Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In Proceedings of the 33rd international conference on machine learning, PMLR, proceedings of machine learning research (Vol.\u00a048, pp. 201\u2013210)."},{"key":"5656_CR17","doi-asserted-by":"crossref","unstructured":"Dwork, C., & Lei, J. (2009). Differential privacy and robust statistics. In STOC\u201909 (pp. 371\u2013380).","DOI":"10.1145\/1536414.1536466"},{"key":"5656_CR18","first-page":"265","volume":"3876","author":"C Dwork","year":"2006","unstructured":"Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006). Calibrating noise to sensitivity in private data analysis. Theory of Cryptography, 3876, 265\u2013284.","journal-title":"Theory of Cryptography"},{"key":"5656_CR19","doi-asserted-by":"crossref","unstructured":"Erlingsson, U., Pihur, V., & Korolova, A. (2014). Rappor: Randomized aggregatable privacy-preserving ordinal response. In CCS\u201914 (pp. 1054\u20131067).","DOI":"10.1145\/2660267.2660348"},{"issue":"1","key":"5656_CR20","doi-asserted-by":"publisher","first-page":"12:1","DOI":"10.1145\/2932707","volume":"49","author":"R Fang","year":"2016","unstructured":"Fang, R., Pouyanfar, S., Yang, Y., Chen, S. C., & Iyengar, S. S. (2016). Computational health informatics in the big data age: A survey. ACM Computing Surveys, 49(1), 12:1\u201312:36. doi: 10.1145\/2932707 .","journal-title":"ACM Computing Surveys"},{"key":"5656_CR21","unstructured":"Glorot, X., Bordes, A., & Bengio, Y. (2011). Deep sparse rectifier neural networks. In Aistats (Vol. 15, p. 275)."},{"issue":"1","key":"5656_CR22","doi-asserted-by":"publisher","first-page":"194","DOI":"10.1186\/1741-7015-11-194","volume":"11","author":"A Gottlieb","year":"2013","unstructured":"Gottlieb, A., Stein, G. Y., Ruppin, E., Altman, R. B., & Sharan, R. (2013). A method for inferring medical diagnoses from patient similarities. BMC Medicine, 11(1), 194. doi: 10.1186\/1741-7015-11-194 .","journal-title":"BMC Medicine"},{"key":"5656_CR23","unstructured":"Harper, T. (2012). A comparative study of function approximators involving neural networks. Thesis, Master of Science, University of Otago. http:\/\/hdl.handle.net\/10523\/2397 ."},{"issue":"1","key":"5656_CR24","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.14778\/1920841.1920970","volume":"3","author":"M Hay","year":"2010","unstructured":"Hay, M., Rastogi, V., Miklau, G., & Suciu, D. (2010). Boosting the accuracy of differentially private histograms through consistency. Proceedings of the VLDB Endowment, 3(1), 1021\u20131032.","journal-title":"Proceedings of the VLDB Endowment"},{"key":"5656_CR25","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. CoRR abs\/1502.01852. \u00a0 http:\/\/arxiv.org\/abs\/1502.01852 ."},{"issue":"7461","key":"5656_CR26","doi-asserted-by":"publisher","first-page":"168","DOI":"10.1038\/nature12346","volume":"500","author":"M Helmstaedter","year":"2013","unstructured":"Helmstaedter, M., Briggman, K. L., Turaga, S. C., Jain, V., Seung, H. S., & Denk, W. (2013). Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), 168\u2013174.","journal-title":"Nature"},{"issue":"8","key":"5656_CR27","doi-asserted-by":"publisher","first-page":"1771","DOI":"10.1162\/089976602760128018","volume":"14","author":"G Hinton","year":"2002","unstructured":"Hinton, G. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8), 1771\u20131800.","journal-title":"Neural Computation"},{"issue":"7","key":"5656_CR28","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527\u20131554. doi: 10.1162\/neco.2006.18.7.1527 .","journal-title":"Neural Computation"},{"issue":"8","key":"5656_CR29","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735\u20131780. doi: 10.1162\/neco.1997.9.8.1735 .","journal-title":"Neural Computation"},{"key":"5656_CR30","unstructured":"Jain, P., Kothari, P., & Thakurta, A. (2012). Differentially private online learning. In COLT\u201912 (pp. 24.1\u201324.34)."},{"key":"5656_CR31","first-page":"1","volume":"236","author":"EW Jamoom","year":"2016","unstructured":"Jamoom, E. W., Yang, N., & Hing, E. (2016). Adoption of certified electronic health record systems and electronic information sharing in physician offices: United states, 2013 and 2014. NCHS Data Brief, 236, 1\u20138.","journal-title":"NCHS Data Brief"},{"key":"5656_CR32","doi-asserted-by":"crossref","unstructured":"Kifer, D., & Machanavajjhala, A. (2011). No free lunch in data privacy. In SIGMOD\u201911 (pp. 193\u2013204).","DOI":"10.1145\/1989323.1989345"},{"key":"5656_CR33","unstructured":"Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems (pp. 1097\u20131105)."},{"issue":"7553","key":"5656_CR34","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436\u2013444. doi: 10.1038\/nature14539 .","journal-title":"Nature"},{"issue":"11","key":"5656_CR35","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11), 2278\u20132324. doi: 10.1109\/5.726791 .","journal-title":"Proceedings of the IEEE"},{"key":"5656_CR36","doi-asserted-by":"crossref","unstructured":"Lee, J., & Clifton, C. (2012). Differential identifiability. In The 18th ACM SIGKDD international conference on knowledge discovery and data mining, KDD \u201912, Beijing, China, 12\u201316 August 2012 (pp. 1041\u20131049).","DOI":"10.1145\/2339530.2339695"},{"issue":"6","key":"5656_CR37","doi-asserted-by":"publisher","first-page":"925","DOI":"10.1109\/3477.735405","volume":"28","author":"T Lee","year":"1998","unstructured":"Lee, T., & Jeng, J. (1998). The chebyshev-polynomials-based unified model neural networks for function approximation. IEEE Transactions on Systems, Man, and Cybernetics, Part B, 28(6), 925\u2013935.","journal-title":"IEEE Transactions on Systems, Man, and Cybernetics, Part B"},{"key":"5656_CR38","doi-asserted-by":"crossref","unstructured":"Lee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009). Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML\u201909 (pp. 609\u2013616).","DOI":"10.1145\/1553374.1553453"},{"key":"5656_CR39","unstructured":"Lei, J. (2011). Differentially private m-estimators. In NIPS (pp. 361\u2013369)."},{"issue":"12","key":"5656_CR40","doi-asserted-by":"publisher","first-page":"i121","DOI":"10.1093\/bioinformatics\/btu277","volume":"30","author":"MKK Leung","year":"2014","unstructured":"Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014). Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), i121\u2013i129. doi: 10.1093\/bioinformatics\/btu277 .","journal-title":"Bioinformatics"},{"issue":"1","key":"5656_CR41","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1109\/TCBB.2014.2330579","volume":"12","author":"H Li","year":"2015","unstructured":"Li, H., Li, X., Ramanathan, M., & Zhang, A. (2015). Prediction and informative risk factor selection of bone diseases. IEEE\/ACM Transactions on Computational Biology and Bioinformatics, 12(1), 79\u201391. doi: 10.1109\/TCBB.2014.2330579 .","journal-title":"IEEE\/ACM Transactions on Computational Biology and Bioinformatics"},{"key":"5656_CR42","doi-asserted-by":"crossref","unstructured":"Li, X., Du, N., Li, H., Li, K., Gao, J., & Zhang, A. (2014). A deep learning approach to link prediction in dynamic networks. In SIAM\u201914 (pp. 289\u2013297).","DOI":"10.1137\/1.9781611973440.33"},{"key":"5656_CR43","doi-asserted-by":"publisher","unstructured":"Liu, S., Liu, S., Cai, W., Pujol, S., Kikinis, R., & Feng, D. (2014). Early diagnosis of Alzheimer\u2019s disease with deep learning. In IEEE 11th international symposium on biomedical imaging, ISBI 2014, Beijing, China (pp. 1015\u20131018). doi: 10.1109\/ISBI.2014.6868045 .","DOI":"10.1109\/ISBI.2014.6868045"},{"issue":"2","key":"5656_CR44","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1021\/ci500747n","volume":"55","author":"J Ma","year":"2015","unstructured":"Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E., & Svetnik, V. (2015). Deep neural nets as a method for quantitative structure-activity relationships. Journal of Chemical Information and Modeling, 55(2), 263\u2013274. doi: 10.1021\/ci500747n .","journal-title":"Journal of Chemical Information and Modeling"},{"key":"5656_CR45","doi-asserted-by":"crossref","unstructured":"Mason, J., & Handscomb, D. (2002). Chebyshev polynomials. Boca Raton: CRC Press. https:\/\/books.google.com\/books?id=8FHf0P3to0UC .","DOI":"10.1201\/9781420036114"},{"key":"5656_CR46","doi-asserted-by":"crossref","unstructured":"McSherry, F., & Mironov, I. (2009). Differentially private recommender systems. In KDD\u201909, ACM.","DOI":"10.1145\/1557019.1557090"},{"key":"5656_CR47","doi-asserted-by":"crossref","unstructured":"McSherry, F., & Talwar, K. (2007a). Mechanism design via differential privacy. In 48th annual IEEE symposium on foundations of computer science (FOCS 2007), 20-23 October 2007, Providence, RI, USA, Proceedings (pp. 94\u2013103).","DOI":"10.1109\/FOCS.2007.66"},{"key":"5656_CR48","doi-asserted-by":"crossref","unstructured":"McSherry, F., & Talwar, K. (2007b). Mechanism design via differential privacy. In FOCS \u201907 (pp. 94\u2013103).","DOI":"10.1109\/FOCS.2007.66"},{"key":"5656_CR49","doi-asserted-by":"publisher","first-page":"26094","DOI":"10.1038\/srep26094","volume":"6","author":"R Miotto","year":"2016","unstructured":"Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016). Deep patient: An unsupervised representation to predict the future of patients from the electronic health records. Scientific Reports, 6, 26094. doi: 10.1038\/srep26094 .","journal-title":"Scientific Reports"},{"key":"5656_CR50","doi-asserted-by":"crossref","unstructured":"Nissim, K., Raskhodnikova, S., & Smith, A. (2007). Smooth sensitivity and sampling in private data analysis. In Proceedings of the thirty-ninth annual ACM symposium on theory of computing (pp. 75\u201384), ACM.","DOI":"10.1145\/1250790.1250803"},{"issue":"07","key":"5656_CR51","doi-asserted-by":"publisher","first-page":"1650,025","DOI":"10.1142\/S0129065716500258","volume":"26","author":"A Ortiz","year":"2016","unstructured":"Ortiz, A., Munilla, J., Grriz, J. M., & Ramrez, J. (2016). Ensembles of deep learning architectures for the early diagnosis of the alzheimers disease. International Journal of Neural Systems, 26(07), 1650,025. doi: 10.1142\/S0129065716500258 .","journal-title":"International Journal of Neural Systems"},{"issue":"2","key":"5656_CR52","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1136\/amiajnl-2013-002159","volume":"21","author":"A Perotte","year":"2014","unstructured":"Perotte, A., Pivovarov, R., Natarajan, K., Weiskopf, N., Wood, F., & Elhadad, N. (2014). Diagnosis code assignment: models and evaluation metrics. Journal of the American Medical Informatics Association, 21(2), 231\u2013237. doi: 10.1136\/amiajnl-2013-002159 .","journal-title":"Journal of the American Medical Informatics Association"},{"issue":"4","key":"5656_CR53","doi-asserted-by":"publisher","first-page":"872","DOI":"10.1093\/jamia\/ocv024","volume":"22","author":"A Perotte","year":"2015","unstructured":"Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015). Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. Journal of the American Medical Informatics Association, 22(4), 872\u2013880. doi: 10.1093\/jamia\/ocv024 .","journal-title":"Journal of the American Medical Informatics Association"},{"key":"5656_CR54","doi-asserted-by":"crossref","unstructured":"Phan, N., Dou, D., Piniewski, B., & Kil, D. (2015a). Social restricted boltzmann machine: Human behavior prediction in health social networks. In ASONAM\u201915 (pp. 424\u2013431).","DOI":"10.1145\/2808719.2808764"},{"key":"5656_CR55","doi-asserted-by":"publisher","unstructured":"Phan, N., Dou, D., Wang, H., Kil, D., & Piniewski, B. (2015b). Ontology-based deep learning for human behavior prediction in health social networks. In Proceedings of the 6th ACM conference on bioinformatics, computational biology and health informatics (pp. 433\u2013442). doi: 10.1145\/2808719.2808764 .","DOI":"10.1145\/2808719.2808764"},{"issue":"1","key":"5656_CR56","doi-asserted-by":"publisher","first-page":"79:1","DOI":"10.1007\/s13278-016-0379-0","volume":"6","author":"N Phan","year":"2016","unstructured":"Phan, N., Dou, D., Piniewski, B., & Kil, D. (2016a). A deep learning approach for human behavior prediction with explanations in health social networks: social restricted boltzmann machine (SRBM+). Social Network Analysis and Mining, 6(1), 79:1\u201379:14. doi: 10.1007\/s13278-016-0379-0 .","journal-title":"Social Network Analysis and Mining"},{"issue":"1","key":"5656_CR57","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1109\/MIS.2015.92","volume":"31","author":"N Phan","year":"2016","unstructured":"Phan, N., Ebrahimi, J., Kil, D., Piniewski, B., & Dou, D. (2016b). Topic-aware physical activity propagation in a health social network. IEEE Intelligent Systems, 31(1), 5\u201314.","journal-title":"IEEE Intelligent Systems"},{"key":"5656_CR58","doi-asserted-by":"crossref","unstructured":"Phan, N., Wang, Y., Wu, X., & Dou, D. (2016c). Differential privacy preservation for deep auto-encoders: An application of human behavior prediction. In AAAI\u201916 (pp. 1309\u20131316).","DOI":"10.1609\/aaai.v30i1.10165"},{"key":"5656_CR59","doi-asserted-by":"publisher","first-page":"229","DOI":"10.3389\/fnins.2014.00229","volume":"8","author":"SM Plis","year":"2014","unstructured":"Plis, S. M., Hjelm, D. R., Salakhutdinov, R., Allen, E. A., Bockholt, H. J., Long, J. D., et al. (2014). Deep learning for neuroimaging: A validation study. Frontiers in Neuroscience, 8, 229. doi: 10.3389\/fnins.2014.00229 .","journal-title":"Frontiers in Neuroscience"},{"key":"5656_CR60","unstructured":"Reed, S. E., Lee, H., Anguelov, D., Szegedy, C., Erhan, D., & Rabinovich, A. (2014). Training deep neural networks on noisy labels with bootstrapping. CoRR abs\/1412.6596."},{"key":"5656_CR61","volume-title":"Chebyshev polynomials form approximation theory to algebra and number theory","author":"TJ Rivlin","year":"1990","unstructured":"Rivlin, T. J. (1990). Chebyshev polynomials form approximation theory to algebra and number theory (2nd ed.). New York: Wiley.","edition":"2"},{"issue":"2","key":"5656_CR62","doi-asserted-by":"publisher","first-page":"451","DOI":"10.1007\/s11886-013-0451-6","volume":"16","author":"M Roumia","year":"2014","unstructured":"Roumia, M., & Steinhubl, S. (2014). Improving cardiovascular outcomes using electronic health records. Current Cardiology Reports, 16(2), 451. doi: 10.1007\/s11886-013-0451-6 .","journal-title":"Current Cardiology Reports"},{"key":"5656_CR63","volume-title":"Principles of mathematical analysis","author":"W Rudin","year":"1976","unstructured":"Rudin, W. (1976). Principles of mathematical analysis. New York: McGraw-Hill."},{"key":"5656_CR64","doi-asserted-by":"crossref","unstructured":"Shokri, R., & Shmatikov, V. (2015). Privacy-preserving deep learning. In CCS\u201915 (pp. 1310\u20131321).","DOI":"10.1145\/2810103.2813687"},{"key":"5656_CR65","unstructured":"Smolensky, P. (1986). Information processing in dynamical systems: Foundations of harmony theory. In Parallel distributed processing: Explorations in the microstructure of cognition (Vol. 1, pp. 194\u2013281)."},{"key":"5656_CR66","doi-asserted-by":"crossref","unstructured":"Song, S., Chaudhuri, K., & Sarwate, A. D. (2013). Stochastic gradient descent with differentially private updates. In GlobalSIP (pp. 245\u2013248).","DOI":"10.1109\/GlobalSIP.2013.6736861"},{"key":"5656_CR67","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). Dropout: A simple way to prevent neural networks from overfitting. Journal of Machine Learning Research, 15, 1929\u20131958. http:\/\/jmlr.org\/papers\/v15\/srivastava14a.html ."},{"key":"5656_CR68","unstructured":"U.S. Department of Health and Human Services. (2016a). Health information technology for economic and clinical health (hitech) act. https:\/\/www.hhs.gov\/hipaa\/for-professionals\/special-topics\/HITECH-act-enforcement-interim-final-rule\/ ."},{"key":"5656_CR69","unstructured":"U.S. Department of Health and Human Services. (2016b). Health insurance portability and accountability act of 1996. http:\/\/www.hhs.gov\/hipaa\/ ."},{"key":"5656_CR70","doi-asserted-by":"publisher","first-page":"387","DOI":"10.14311\/NNW.2012.22.023","volume":"4","author":"M Vlcek","year":"2012","unstructured":"Vlcek, M. (2012). Chebyshev polynomial approximation for activation sigmoid function. Neural Network World, 4, 387\u2013393.","journal-title":"Neural Network World"},{"key":"5656_CR71","doi-asserted-by":"crossref","unstructured":"Wang, Y., Wu, X., & Wu, L. (2013). Differential privacy preserving spectral graph analysis. In PAKDD (2) (pp. 329\u2013340).","DOI":"10.1007\/978-3-642-37456-2_28"},{"key":"5656_CR72","unstructured":"Wikipedia. (2016). Activation function. https:\/\/en.wikipedia.org\/wiki\/Activation_function ."},{"issue":"6 Suppl","key":"5656_CR73","doi-asserted-by":"publisher","first-page":"S106","DOI":"10.1097\/MLR.0b013e3181de9e17","volume":"48","author":"J Wu","year":"2010","unstructured":"Wu, J., Roy, J., & Stewart, W. F. (2010). Prediction modeling using EHR data: Challenges, strategies, and a comparison of machine learning approaches. Medical Care, 48(6 Suppl), S106\u2013S113. doi: 10.1097\/mlr.0b013e3181de9e17 .","journal-title":"Medical Care"},{"key":"5656_CR74","doi-asserted-by":"crossref","unstructured":"Xiao, X., Wang, G., & Gehrke, J. (2010). Differential privacy via wavelet transforms. In ICDE\u201910 (pp. 225\u2013236).","DOI":"10.1109\/ICDE.2010.5447831"},{"issue":"6218","key":"5656_CR75","doi-asserted-by":"publisher","first-page":"1254806","DOI":"10.1126\/science.1254806","volume":"347","author":"HY Xiong","year":"2015","unstructured":"Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K. C., et al. (2015). The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218), 1254806. doi: 10.1126\/science.1254806 .","journal-title":"Science"},{"issue":"11","key":"5656_CR76","first-page":"1364","volume":"5","author":"J Zhang","year":"2012","unstructured":"Zhang, J., Zhang, Z., Xiao, X., Yang, Y., & Winslett, M. (2012). Functional mechanism: Regression analysis under differential privacy. PVLDB, 5(11), 1364\u20131375.","journal-title":"PVLDB"}],"container-title":["Machine Learning"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/article\/10.1007\/s10994-017-5656-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-017-5656-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/s10994-017-5656-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,24]],"date-time":"2023-08-24T13:42:51Z","timestamp":1692884571000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/s10994-017-5656-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,7,13]]},"references-count":76,"journal-issue":{"issue":"9-10","published-print":{"date-parts":[[2017,10]]}},"alternative-id":["5656"],"URL":"https:\/\/doi.org\/10.1007\/s10994-017-5656-2","relation":{},"ISSN":["0885-6125","1573-0565"],"issn-type":[{"value":"0885-6125","type":"print"},{"value":"1573-0565","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,7,13]]},"assertion":[{"value":"18 November 2016","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 June 2017","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 July 2017","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}