1. bookVolume 2019 (2019): Issue 3 (July 2019)
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
16 Apr 2015
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch
access type Open Access

SecureNN: 3-Party Secure Computation for Neural Network Training

Online veröffentlicht: 12 Jul 2019
Seitenbereich: 26 - 49
Eingereicht: 30 Nov 2018
Akzeptiert: 16 Mar 2019
Zeitschriftendaten
License
Format
Zeitschrift
Erstveröffentlichung
16 Apr 2015
Erscheinungsweise
4 Hefte pro Jahr
Sprachen
Englisch

Neural Networks (NN) provide a powerful method for machine learning training and inference. To effectively train, it is desirable for multiple parties to combine their data – however, doing so conflicts with data privacy. In this work, we provide novel three-party secure computation protocols for various NN building blocks such as matrix multiplication, convolutions, Rectified Linear Units, Maxpool, normalization and so on. This enables us to construct three-party secure protocols for training and inference of several NN architectures such that no single party learns any information about the data. Experimentally, we implement our system over Amazon EC2 servers in different settings. Our work advances the state-of-the-art of secure computation for neural networks in three ways:

[1] Eigen Library. http://eigen.tuxfamily.org/. Version: 3.3.3.Search in Google Scholar

[2] Fixed-point data type. http://dec64.com. Last Updted: 2018-01-20.Search in Google Scholar

[3] MNIST database. http://yann.lecun.com/exdb/mnist/. Accessed: 2017-09-24.Search in Google Scholar

[4] Stanford CS231n: Convolutional Neural Networks for Visual Recognition. http://cs231n.github.io/convolutionalnetworks/.Search in Google Scholar

[5] Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (GDPR). Official Journal of the European Union, L119, May 2016.Search in Google Scholar

[6] Toshinori Araki, Jun Furukawa, Yehuda Lindell, Ariel Nof, and Kazuma Ohara. High-throughput semi-honest secure three-party computation with an honest majority. In ACM CCS, 2016.Search in Google Scholar

[7] Donald Beaver. Efficient multiparty protocols using circuit randomization. In Annual International Cryptology Conference, pages 420–432. Springer, 1991.Search in Google Scholar

[8] Michael Ben-Or, Shafi Goldwasser, and Avi Wigderson. Completeness theorems for non-cryptographic fault-tolerant distributed computation (extended abstract). In ACM STOC, 1988.Search in Google Scholar

[9] Dan Bogdanov, Sven Laur, and Jan Willemson. Sharemind: A framework for fast privacy-preserving computations. In ESORICS, pages 192–206, 2008.Search in Google Scholar

[10] Dan Bogdanov, Margus Niitsoo, Tomas Toft, and Jan Willemson. High-performance secure multi-party computation for data mining applications. International Journal of Information Security, 11(6):403–418, 2012.Search in Google Scholar

[11] Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser. Machine learning classification over encrypted data. In NDSS, 2015.Search in Google Scholar

[12] R. Canetti. Universally composable security: A new paradigm for cryptographic protocols. In Proceedings of the 42Nd IEEE Symposium on Foundations of Computer Science, FOCS ’01, pages 136–, 2001.Search in Google Scholar

[13] Ran Canetti. Security and composition of multiparty cryptographic protocols. Journal of CRYPTOLOGY, 13(1):143–202, 2000.Search in Google Scholar

[14] Octavian Catrina and Sebastiaan de Hoogh. Improved primitives for secure multiparty integer computation. In Security and Cryptography for Networks, 7th International Conference, SCN 2010, Amalfi, Italy, September 13–15, 2010. Proceedings, pages 182–199, 2010.Search in Google Scholar

[15] Centers for Medicare & Medicaid Services. The Health Insurance Portability and Accountability Act of 1996 (HIPAA). Online at http://www.cms.hhs.gov/hipaa/, 1996.Search in Google Scholar

[16] Hervé Chabanne, Amaury de Wargny, Jonathan Milgram, Constance Morel, and Emmanuel Prouff. Privacy-preserving classification on deep neural network. Cryptology ePrint Archive, Report 2017/035, 2017. https://eprint.iacr.org/2017/035.Search in Google Scholar

[17] David Chaum, Claude Crépeau, and Ivan Damgård. Multiparty unconditionally secure protocols (extended abstract). In ACM STOC, 1988.Search in Google Scholar

[18] Koji Chida, Daniel Genkin, Koki Hamada, Dai Ikarashi, Ryo Kikuchi, Yehuda Lindell, and Ariel Nof. Fast large-scale honest-majority mpc for malicious adversaries. In Crypto, pages 34–64, 2018.Search in Google Scholar

[19] Benny Chor and Eyal Kushilevitz. A zero-one law for boolean privacy. SIAM J. Discrete Math., 4(1), 1991.Search in Google Scholar

[20] Ivan Damgård, Martin Geisler, and Mikkel Krøigaard. Homomorphic encryption and secure comparison. In IJACT, 2008.Search in Google Scholar

[21] Daniel Demmler, Thomas Schneider, and Michael Zohner. ABY – A framework for efficient mixed-protocol secure twoparty computation. In NDSS, 2015.Search in Google Scholar

[22] Cynthia Dwork, Aaron Roth, et al. The algorithmic foundations of differential privacy. 2014.Search in Google Scholar

[23] Jun Furukawa, Yehuda Lindell, Ariel Nof, and Or Weinstein. High-throughput secure three-party computation for malicious adversaries and an honest majority. In IACR Eurocrypt, 2017.Search in Google Scholar

[24] Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin E. Lauter, Michael Naehrig, and John Wernsing. CryptoNets: Applying neural networks to encrypted data with high throughput and accuracy. In ICML, 2016.Search in Google Scholar

[25] Oded Goldreich, Silvio Micali, and Avi Wigderson. How to play any mental game or a completeness theorem for protocols with honest majority. In ACM STOC, 1987.Search in Google Scholar

[26] Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. Gazelle: A low latency framework for secure neural network inference. In Usenix Security, 2018.Search in Google Scholar

[27] Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.Search in Google Scholar

[28] Yehuda Lindell and Ariel Nof. A framework for constructing fast MPC over arithmetic circuits with malicious adversaries and an honest-majority. In ACM CCS, 2017.Search in Google Scholar

[29] Yehuda Lindell and Benny Pinkas. Privacy preserving data mining. In Annual International Cryptology Conference, pages 36–54. Springer, 2000.Search in Google Scholar

[30] Jian Liu, Mika Juuti, Yao Lu, and N. Asokan. Oblivious neural network predictions via MiniONN transformations. In ACM CCS, 2017.Search in Google Scholar

[31] Payman Mohassel and Peter Rindal. ABY3: A mixed protocol framework for machine learning. In ACM CCS, 2018.Search in Google Scholar

[32] Payman Mohassel, Mike Rosulek, and Ye Zhang. Fast and secure three-party computation: The garbled circuit approach. In ACM CCS, 2015.Search in Google Scholar

[33] Payman Mohassel and Yupeng Zhang. SecureML: A system for scalable privacy-preserving machine learning. In ieeeoakland, 2017.Search in Google Scholar

[34] Valeria Nikolaenko, Stratis Ioannidis, Udi Weinsberg, Marc Joye, Nina Taft, and Dan Boneh. Privacy-preserving matrix factorization. In ACM CCS, pages 801–812, 2013.Search in Google Scholar

[35] Takashi Nishide and Kazuo Ohta. Multiparty computation for interval, equality, and comparison without bitdecomposition protocol. In PKC, 2007.Search in Google Scholar

[36] M. Sadegh Riazi, Christian Weinert, Oleksandr Tkachenko, Ebrahim M. Songhori, Thomas Schneider, and Farinaz Koushanfar. Chameleon: A hybrid secure computation framework for machine learning applications. In AsiaCCS, 2018.Search in Google Scholar

[37] Reza Shokri and Vitaly Shmatikov. Privacy-preserving deep learning. In Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, pages 1310–1321. ACM, 2015.Search in Google Scholar

[38] Wagh, Sameer and Gupta, Divya and Chandran, Nishanth. SecureNN: 3-Party Secure Computation for Neural Network Training. https://eprint.iacr.org/2018/442.pdf, 2019.Search in Google Scholar

[39] David J. Wu, Tony Feng, Michael Naehrig, and Kristin E. Lauter. Privately evaluating decision trees and random forests. PoPETs, 2016, 2016.Search in Google Scholar

[40] Andrew Chi-Chih Yao. How to generate and exchange secrets (extended abstract). In IEEE FOCS, 1986.Search in Google Scholar

[41] Xiangxin Zhu, Carl Vondrick, Charless Fowlkes, and Deva Ramanan. Do we need more training data? In International Journal of Computer Vision, 2016.Search in Google Scholar

Recommended articles from Trend MD

Plan your remote conference with Sciendo