{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T17:12:08Z","timestamp":1780420328886,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":60,"publisher":"ACM","license":[{"start":{"date-parts":[[2017,10,30]],"date-time":"2017-10-30T00:00:00Z","timestamp":1509321600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"The Finnish Funding Agency for Innovation (TEKES)","award":["3881\/31\/2016"],"award-info":[{"award-number":["3881\/31\/2016"]}]},{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"publisher","award":["274951"],"award-info":[{"award-number":["274951"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2017,10,30]]},"DOI":"10.1145\/3133956.3134056","type":"proceedings-article","created":{"date-parts":[[2017,10,27]],"date-time":"2017-10-27T12:48:18Z","timestamp":1509108498000},"page":"619-631","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":499,"title":["Oblivious Neural Network Predictions via MiniONN Transformations"],"prefix":"10.1145","author":[{"given":"Jian","family":"Liu","sequence":"first","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mika","family":"Juuti","sequence":"additional","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yao","family":"Lu","sequence":"additional","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"N.","family":"Asokan","sequence":"additional","affiliation":[{"name":"Aalto University, Espoo, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2017,10,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2976749.2978318"},{"key":"e_1_3_2_2_2_1","unstructured":"Mart\u00edn Abadi et al. 2016. TensorFlow: A System for Large-Scale Machine Learning. In 12th USENIX Symposium on Operating Systems Design and Implementation (OSDI 16). USENIX Association GA 265--283. https:\/\/www.usenix.org\/ conference\/osdi16\/technical-sessions\/presentation\/abadi"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.qref.2007.04.001"},{"key":"e_1_3_2_2_4_1","volume-title":"Holmes","author":"Aslett Louis J. M.","year":"2015","unstructured":"Louis J. M. Aslett, Pedro M. Esperan\u00e7a, and Chris C. Holmes. 2015. Encrypted statistical machine learning: new privacy preserving methods. CoRR abs\/1508.06845 (2015). http:\/\/arxiv.org\/abs\/1508.06845"},{"key":"e_1_3_2_2_5_1","volume-title":"Holmes","author":"Aslett Louis J. M.","year":"2015","unstructured":"Louis J. M. Aslett, Pedro M. Esperan\u00e7a, and Chris C. Holmes. 2015. A review of homomorphic encryption and software tools for encrypted statistical machine learning. CoRR abs\/1508.06574 (2015). http:\/\/arxiv.org\/abs\/1508.06574"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-04444-1_26"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/1161366.1161393"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-46766-1_34"},{"key":"e_1_3_2_2_9_1","volume-title":"Proc. 9th Python in Science Conf. 1--7.","author":"James","unstructured":"James Bergstra et al. 2010. Theano: A CPU and GPU math compiler in Python. In Proc. 9th Python in Science Conf. 1--7."},{"key":"e_1_3_2_2_10_1","volume-title":"Pattern Recognition and Machine Learning (Information Science and Statistics)","author":"Bishop Christopher M.","unstructured":"Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-30428-6_9"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-88313-5_13"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-45239-0_4"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23241"},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/1315245.1315307"},{"key":"e_1_3_2_2_17_1","volume-title":"Batch-normalized Maxout Network in Network. CoRR abs\/1511.02583","author":"Chang Jia-Ren","year":"2015","unstructured":"Jia-Ren Chang and Yong-Sheng Chen. 2015. Batch-normalized Maxout Network in Network. CoRR abs\/1511.02583 (2015). http:\/\/arxiv.org\/abs\/1511.02583"},{"key":"e_1_3_2_2_18_1","volume-title":"Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft.","author":"Chellapilla Kumar","year":"2006","unstructured":"Kumar Chellapilla, Sidd Puri, and Patrice Simard. 2006. High performance convolutional neural networks for document processing. In Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft."},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2354409.2354694"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/TASL.2011.2134090"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2015.23113"},{"key":"e_1_3_2_2_22_1","volume-title":"Curve and surface fitting with splines","author":"Dierckx Paul","unstructured":"Paul Dierckx. 1995. Curve and surface fitting with splines. Oxford University Press."},{"key":"e_1_3_2_2_23_1","volume-title":"Manual for using homomorphic encryption for bioinformatics. Microsoft Research","author":"Dowlin Nathan","year":"2015","unstructured":"Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2015. Manual for using homomorphic encryption for bioinformatics. Microsoft Research (2015)."},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-39568-7_2"},{"key":"e_1_3_2_2_25_1","volume-title":"Proceedings of the International Conference on Machine Learning.","author":"Fakoor Rasool","year":"2013","unstructured":"Rasool Fakoor, Faisal Ladhak, Azade Nazi, and Manfred Huber. 2013. Using deep learning to enhance cancer diagnosis and classification. In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_2_2_26_1","volume-title":"Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In 23rd USENIX Security Symposium (USENIX Security 14)","author":"Fredrikson Matthew","year":"2014","unstructured":"Matthew Fredrikson, Eric Lantz, Somesh Jha, Simon Lin, David Page, and Thomas Ristenpart. 2014. Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. In 23rd USENIX Security Symposium (USENIX Security 14). USENIX Association, 17--32. https:\/\/www.usenix.org\/conference\/usenixsecurity14\/technical-sessions\/presentation\/fredrikson_matthew"},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.1145\/1835804.1835868"},{"key":"e_1_3_2_2_28_1","volume-title":"Proceedings of The 33rd International Conference on Machine Learning. 201--210","author":"Gilad-Bachrach Ran","year":"2016","unstructured":"Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy. In Proceedings of The 33rd International Conference on Machine Learning. 201--210."},{"key":"e_1_3_2_2_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/28395.28420"},{"key":"e_1_3_2_2_30_1","volume-title":"Deep Learning","author":"Goodfellow Ian","unstructured":"Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http:\/\/www.deeplearningbook.org."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-37682-5_1"},{"key":"e_1_3_2_2_32_1","volume-title":"Fractional Max-Pooling. CoRR abs\/1412.6071","author":"Graham Benjamin","year":"2014","unstructured":"Benjamin Graham. 2014. Fractional Max-Pooling. CoRR abs\/1412.6071 (2014). http:\/\/arxiv.org\/abs\/1412.6071"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_2_34_1","unstructured":"Eric Jones Travis Oliphant P Peterson et al. 2001. SciPy: Open source scientific tools for Python. (2001)."},{"key":"e_1_3_2_2_35_1","volume-title":"Nature: Computer science: The learning machines.","author":"Jones Nicola","year":"2014","unstructured":"Nicola Jones. 2014. Nature: Computer science: The learning machines. (2014). http:\/\/www.nature.com\/news\/computer-science-the-learning-machines-1. 14481."},{"key":"e_1_3_2_2_36_1","unstructured":"Alex Krizhevsky and Geoffrey Hinton. 2009. Learning multiple layers of features from tiny images. (2009). http:\/\/citeseerx.ist.psu.edu\/viewdoc\/download?doi=10. 1.1.222.9220&rep=rep1&type=pdf."},{"key":"e_1_3_2_2_37_1","volume-title":"Advances in Neural Information Processing Systems 25","author":"Krizhevsky Alex","unstructured":"Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in Neural Information Processing Systems 25, F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger (Eds.). Curran Associates, Inc., 1097--1105. http:\/\/papers.nips.cc\/paper\/ 4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf"},{"key":"e_1_3_2_2_38_1","unstructured":"Yann LeCun Corinna Cortes and Christopher JC Burges. 1998. The MNIST database of handwritten digits. (1998). http:\/\/yann.lecun.com\/exdb\/mnist\/."},{"key":"e_1_3_2_2_39_1","volume-title":"Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016","author":"Lee Chen-Yu","year":"2016","unstructured":"Chen-Yu Lee, Patrick W. Gallagher, and Zhuowen Tu. 2016. Generalizing Pooling Functions in Convolutional Neural Networks: Mixed, Gated, and Tree. In Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, AISTATS 2016, Cadiz, Spain, May 9-11, 2016. 464--472. http:\/\/jmlr.org\/proceedings\/papers\/v51\/lee16a.html"},{"key":"e_1_3_2_2_40_1","volume-title":"On the limited memory BFGS method for large scale optimization. Mathematical programming 45, 1","author":"Liu Dong C","year":"1989","unstructured":"Dong C Liu and Jorge Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical programming 45, 1 (1989), 503--528."},{"key":"e_1_3_2_2_41_1","volume-title":"Mary Ann Marcinkiewicz, and Beatrice Santorini","author":"Marcus Mitchell P","year":"1993","unstructured":"Mitchell P Marcus, Mary Ann Marcinkiewicz, and Beatrice Santorini. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational linguistics 19, 2 (1993), 313--330."},{"key":"e_1_3_2_2_42_1","unstructured":"Tom\u00e1\u0161 Mikolov et al. 2012. Subword language modeling with neural networks. (2012). http:\/\/www.fit.vutbr.cz\/imikolov\/rnnlm\/char.pdf"},{"key":"e_1_3_2_2_43_1","volume-title":"All you need is a good init. CoRR abs\/1511.06422","author":"Mishkin Dmytro","year":"2015","unstructured":"Dmytro Mishkin and Jiri Matas. 2015. All you need is a good init. CoRR abs\/1511.06422 (2015). http:\/\/arxiv.org\/abs\/1511.06422"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.12"},{"key":"e_1_3_2_2_45_1","volume-title":"Machine learning: a probabilistic perspective","author":"Murphy Kevin P","unstructured":"Kevin P Murphy. 2012. Machine learning: a probabilistic perspective. MIT press."},{"key":"e_1_3_2_2_46_1","volume-title":"Oblivious Multi-Party Machine Learning on Trusted Processors. In 25th USENIX Security Symposium (USENIX Security 16)","author":"Ohrimenko Olga","year":"2016","unstructured":"Olga Ohrimenko, Felix Schuster, Cedric Fournet, Aastha Mehta, Sebastian Nowozin, Kapil Vaswani, and Manuel Costa. 2016. Oblivious Multi-Party Machine Learning on Trusted Processors. In 25th USENIX Security Symposium (USENIX Security 16). USENIX Association, Austin, TX, 619--636. https:\/\/www.usenix.org\/conference\/usenixsecurity16\/technical-sessions\/presentation\/ohrimenko"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","unstructured":"C. Orlandi A. Piva and M. Barni. 2007. Oblivious Neural Network Computing via Homomorphic Encryption. EURASIP J. Inf. Secur. 2007 Article 18 (Jan. 2007) 10 pages. https:\/\/doi.org\/10.1155\/2007\/37343","DOI":"10.1155\/2007"},{"key":"e_1_3_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1007\/3-540-48910-X_16"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-662-48051-9_13"},{"key":"e_1_3_2_2_50_1","volume-title":"DeepSecure: Scalable Provably-Secure Deep Learning. CoRR abs\/1705.08963","author":"Rouhani Bita Darvish","year":"2017","unstructured":"Bita Darvish Rouhani, M. Sadegh Riazi, and Farinaz Koushanfar. 2017. DeepSecure: Scalable Provably-Secure Deep Learning. CoRR abs\/1705.08963 (2017). http:\/\/arxiv.org\/abs\/1705.08963"},{"key":"e_1_3_2_2_51_1","volume-title":"APAC: Augmented PAttern Classification with Neural Networks. CoRR abs\/1505.03229","author":"Sato Ikuro","year":"2015","unstructured":"Ikuro Sato, Hiroki Nishimura, and Kensuke Yokoi. 2015. APAC: Augmented PAttern Classification with Neural Networks. CoRR abs\/1505.03229 (2015). http:\/\/arxiv.org\/abs\/1505.03229"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1145\/2810103.2813687"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10623-012-9720-4"},{"key":"e_1_3_2_2_55_1","volume-title":"Riedmiller","author":"Springenberg Jost Tobias","year":"2014","unstructured":"Jost Tobias Springenberg, Alexey Dosovitskiy, Thomas Brox, and Martin A. Riedmiller. 2014. Striving for Simplicity: The All Convolutional Net. CoRR abs\/1412.6806 (2014). http:\/\/arxiv.org\/abs\/1412.6806"},{"key":"e_1_3_2_2_56_1","volume-title":"25th USENIX Security Symposium (USENIX Security 16)","author":"Tram\u00e8r Florian","year":"2016","unstructured":"Florian Tram\u00e8r, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016. Stealing Machine Learning Models via Prediction APIs. In 25th USENIX Security Symposium (USENIX Security 16). USENIX Association, Austin, TX, 601--618. https:\/\/www.usenix.org\/conference\/usenixsecurity16\/technical-sessions\/presentation\/tramer"},{"key":"e_1_3_2_2_57_1","volume-title":"Proceedings of the 30th International Conference on Machine Learning (ICML-13), Sanjoy Dasgupta and David Mcallester (Eds.). JMLR Workshop and Conference Proceedings, 1058--1066","author":"Wan Li","year":"2013","unstructured":"Li Wan, Matthew Zeiler, Sixin Zhang, Yann L. Cun, and Rob Fergus. 2013. Regularization of Neural Networks using DropConnect. In Proceedings of the 30th International Conference on Machine Learning (ICML-13), Sanjoy Dasgupta and David Mcallester (Eds.). JMLR Workshop and Conference Proceedings, 1058--1066. http:\/\/jmlr.org\/proceedings\/papers\/v28\/wan13.pdf"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1515\/popets-2016-0043"},{"key":"e_1_3_2_2_59_1","volume-title":"Foundations of Computer Science (FOCS'82)","author":"Chi-Chih Yao Andrew","unstructured":"Andrew Chi-Chih Yao. 1982. Protocols for Secure Computations (Extended Abstract). In Foundations of Computer Science (FOCS'82). IEEE, 160--164."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1109\/SFCS.1986.25"},{"key":"e_1_3_2_2_61_1","volume-title":"Recurrent neural network regularization. CoRR abs\/1409.2329","author":"Zaremba Wojciech","year":"2014","unstructured":"Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. CoRR abs\/1409.2329 (2014). http:\/\/arxiv.org\/abs\/1409.2329"}],"event":{"name":"CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security","location":"Dallas Texas USA","acronym":"CCS '17","sponsor":["SIGSAC ACM Special Interest Group on Security, Audit, and Control"]},"container-title":["Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3133956.3134056","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3133956.3134056","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T02:11:03Z","timestamp":1750212663000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3133956.3134056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,10,30]]},"references-count":60,"alternative-id":["10.1145\/3133956.3134056","10.1145\/3133956"],"URL":"https:\/\/doi.org\/10.1145\/3133956.3134056","relation":{},"subject":[],"published":{"date-parts":[[2017,10,30]]},"assertion":[{"value":"2017-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}