research-article

Oblivious Neural Network Predictions via MiniONN Transformations

Authors:

N. AsokanAuthors Info & Claims

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

Pages 619 - 631

https://doi.org/10.1145/3133956.3134056

Published: 30 October 2017 Publication History

Abstract

Machine learning models hosted in a cloud service are increasingly popular but risk privacy: clients sending prediction requests to the service need to disclose potentially sensitive information. In this paper, we explore the problem of privacy-preserving predictions: after each prediction, the server learns nothing about clients' input and clients learn nothing about the model.

We present MiniONN, the first approach for transforming an existing neural network to an oblivious neural network supporting privacy-preserving predictions with reasonable efficiency. Unlike prior work, MiniONN requires no change to how models are trained. To this end, we design oblivious protocols for commonly used operations in neural network prediction models. We show that MiniONN outperforms existing work in terms of response latency and message sizes. We demonstrate the wide applicability of MiniONN by transforming several typical neural network models trained from standard datasets.

Supplemental Material

MP4 File

Download
1992.61 MB

References

[1]

Martín Abadi et al. 2016. Deep Learning with Differential Privacy. In Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (CCS '16). ACM, New York, NY, USA, 308--318. https://doi.org/10.1145/2976749.2978318

Digital Library

[2]

Martín 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

[3]

Eliana Angelini, Giacomo di Tollo, and Andrea Roli. 2008. A neural network approach for credit risk evaluation. The quarterly review of economics and finance 48, 4 (2008), 733--755.

[4]

Louis J. M. Aslett, Pedro M. Esperança, and Chris C. Holmes. 2015. Encrypted statistical machine learning: new privacy preserving methods. CoRR abs/1508.06845 (2015). http://arxiv.org/abs/1508.06845

[5]

Louis J. M. Aslett, Pedro M. Esperança, 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

[6]

Mauro Barni et al. 2009. Secure Evaluation of Private Linear Branching Programs with Medical Applications. In Computer Security - ESORICS 2009, 14th European Symposium on Research in Computer Security. Springer, Saint-Malo, France, 424--439. http://dx.doi.org/10.1007/978-3-642-04444-1_26

[7]

M. Barni, C. Orlandi, and A. Piva. 2006. A Privacy-preserving Protocol for Neuralnetwork-based Computation. In Proceedings of the 8th Workshop on Multimedia and Security (MM&Sec '06). ACM, New York, NY, USA, 146--151. https://doi.org/ 10.1145/1161366.1161393

Digital Library

[8]

Donald Beaver. 1991. Efficient Multiparty Protocols Using Circuit Randomization. In Advances in Cryptology - CRYPTO '91, 11th Annual International Cryptology Conference, Santa Barbara, California, USA, August 11--15, 1991, Proceedings (Lecture Notes in Computer Science), Vol. 576. Springer, 420--432. https: doi.org/10.1007/3-540-46766-1_34

[9]

James Bergstra et al. 2010. Theano: A CPU and GPU math compiler in Python. In Proc. 9th Python in Science Conf. 1--7.

[10]

Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, USA.

Digital Library

[11]

Dan Bogdanov, Roman Jagomägis, and Sven Laur. 2012. A Universal Toolkit for Cryptographically Secure Privacy-preserving Data Mining. In Proceedings of the 2012 Pacific Asia Conference on Intelligence and Security Informatics (PAISI'12). Springer-Verlag, Berlin, Heidelberg, 112--126. https://doi.org/10.1007/ 978-3-642-30428-6_9

Digital Library

[12]

Dan Bogdanov, Sven Laur, and Jan Willemson. 2008. Sharemind: A Framework for Fast Privacy-Preserving Computations. Springer Berlin Heidelberg, Berlin, Heidelberg, 192--206. https://doi.org/10.1007/978-3-540-88313-5_13

Digital Library

[13]

Joppe W. Bos, Kristin Lauter, Jake Loftus, and Michael Naehrig. 2013. Improved Security for a Ring-Based Fully Homomorphic Encryption Scheme. Springer Berlin Heidelberg, Berlin, Heidelberg, 45--64. https://doi.org/10.1007/978-3-642-45239-0_4

Digital Library

[14]

Raphael Bost, Raluca Ada Popa, Stephen Tu, and Shafi Goldwasser. 2015. Machine Learning Classification over Encrypted Data. In 22nd Annual Network and Distributed System Security Symposium, NDSS 2015, San Diego, California, USA, February 8-11, 2015. http://www.internetsociety.org/doc/ machine-learning-classification-over-encrypted-data

[15]

Justin Brickell, Donald E. Porter, Vitaly Shmatikov, and Emmett Witchel. 2007. Privacy-preserving remote diagnostics. In Proceedings of the 2007 ACM Conference on Computer and Communications Security, CCS 2007, Alexandria, Virginia, USA, October 28-31, 2007. 498--507. http://doi.acm.org/10.1145/1315245.1315307

Digital Library

[16]

Hervé Chabanne, Amaury de Wargny, Jonathan Milgram, Constance Morel, and Emmanuel Prouff. 2017. Privacy-Preserving Classification on Deep Neural Network. Cryptology ePrint Archive, Report 2017/035. (2017). http://eprint.iacr.org/2017/035.

[17]

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

[18]

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.

[19]

Dan Ciregan, Ueli Meier, and Jürgen Schmidhuber. 2012. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. IEEE, 3642--3649.

Digital Library

[20]

G. E. Dahl, D. Yu, L. Deng, and A. Acero. 2012. Context-Dependent PreTrained Deep Neural Networks for Large-Vocabulary Speech Recognition. IEEE Transactions on Audio, Speech, and Language Processing 20, 1 (Jan 2012), 30--42. https://doi.org/10.1109/TASL.2011.2134090

Digital Library

[21]

Daniel Demmler, Thomas Schneider, and Michael Zohner. 2015. ABY-A Framework for Efficient Mixed-Protocol Secure Two-Party Computation. In 22nd Annual Network and Distributed System Security Symposium, NDSS 2015, San Diego, California, USA, February 8-11, 2015.

[22]

Paul Dierckx. 1995. Curve and surface fitting with splines. Oxford University Press.

[23]

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).

[24]

Taher ElGamal. 1985. A Public Key Cryptosystem and a Signature Scheme Based on Discrete Logarithms. In CRYPTO (LNCS), Vol. 196. Springer, 10--18. https://doi.org/10.1007/3-540-39568-7_2

[25]

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.

[26]

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

[27]

Arik Friedman and Assaf Schuster. 2010. Data Mining with Differential Privacy. In Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '10). ACM, New York, NY, USA, 493--502. https://doi.org/10.1145/1835804.1835868

Digital Library

[28]

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.

Digital Library

[29]

O. Goldreich, S. Micali, and A. Wigderson. 1987. How to Play ANY Mental Game. In Proceedings of the Nineteenth Annual ACM Symposium on Theory of Computing (STOC '87). ACM, New York, NY, USA, 218--229. https://doi.org/10.1145/28395.28420

Digital Library

[30]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press. http://www.deeplearningbook.org.

Digital Library

[31]

Thore Graepel, Kristin E. Lauter, and Michael Naehrig. 2012. ML Confidential: Machine Learning on Encrypted Data. In Information Security and Cryptology - ICISC 2012 - 15th International Conference, Seoul, Korea, November 28-30, 2012, Revised Selected Papers. 1--21. http://dx.doi.org/10.1007/978-3-642-37682-5_1

Digital Library

[32]

Benjamin Graham. 2014. Fractional Max-Pooling. CoRR abs/1412.6071 (2014). http://arxiv.org/abs/1412.6071

[33]

Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural computation 9, 8 (1997), 1735--1780.

Digital Library

[34]

Eric Jones, Travis Oliphant, P Peterson, et al. 2001. SciPy: Open source scientific tools for Python. (2001).

[35]

Nicola Jones. 2014. Nature: Computer science: The learning machines. (2014). http://www.nature.com/news/computer-science-the-learning-machines-1. 14481.

[36]

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.

[37]

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

Digital Library

[38]

Yann LeCun, Corinna Cortes, and Christopher JC Burges. 1998. The MNIST database of handwritten digits. (1998). http://yann.lecun.com/exdb/mnist/.

[39]

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

[40]

Dong C Liu and Jorge Nocedal. 1989. On the limited memory BFGS method for large scale optimization. Mathematical programming 45, 1 (1989), 503--528.

[41]

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.

Digital Library

[42]

Tomáš Mikolov et al. 2012. Subword language modeling with neural networks. (2012). http://www.fit.vutbr.cz/imikolov/rnnlm/char.pdf

[43]

Dmytro Mishkin and Jiri Matas. 2015. All you need is a good init. CoRR abs/1511.06422 (2015). http://arxiv.org/abs/1511.06422

[44]

Payman Mohassel and Yupeng Zhang. 2017. SecureML: A System for Scalable Privacy-Preserving Machine Learning. In IEEE Symposium on Security and Privacy (S&P'17). IEEE. http://ieeexplore.ieee.org/document/7958569.

[45]

Kevin P Murphy. 2012. Machine learning: a probabilistic perspective. MIT press.

Digital Library

[46]

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

Digital Library

[47]

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

[48]

Pascal Paillier. 1999. Public-Key Cryptosystems Based on Composite Degree Residuosity Classes. In EUROCRYPT (LNCS), Jacques Stern (Ed.), Vol. 1592. Springer, 223--238. https://doi.org/10.1007/3-540-48910-X_16

[49]

Pille Pullonen and Sander Siim. 2015. Combining Secret Sharing and Garbled Circuits for Efficient Private IEEE 754 Floating-Point Computations. In Financial Cryptography and Data Security - FC 2015 International Workshops, BITCOIN, WAHC, and Wearable, San Juan, Puerto Rico, January 30, 2015, Revised Selected Papers. 172--183. https://doi.org/10.1007/978-3-662-48051-9_13

[50]

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

[51]

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

[52]

Reza Shokri and Vitaly Shmatikov. 2015. Privacy-Preserving Deep Learning. In Proceedings of the 22Nd ACM SIGSAC Conference on Computer and Communications Security (CCS '15). ACM, New York, NY, USA, 1310--1321. https://doi.org/10.1145/2810103.2813687

Digital Library

[53]

Reza Shokri, Marco Stronati, Congzheng Song, and Vitaly Shmatikov. 2017. Membership Inference Attacks Against Machine Learning Models. In 2017 IEEE Symposium on Security and Privacy, SP 2017, San Jose, CA, USA, May 22-26, 2017. IEEE, 3--18. https://doi.org/10.1109/SP.2017.41

[54]

N. P. Smart and F. Vercauteren. 2014. Fully homomorphic SIMD operations. Designs, Codes and Cryptography 71, 1 (2014), 57--81. https://doi.org/10.1007/s10623-012-9720-4

Digital Library

[55]

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

[56]

Florian Tramèr, 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

Digital Library

[57]

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

[58]

David J. Wu, Tony Feng, Michael Naehrig, and Kristin E. Lauter. 2016. Privately Evaluating Decision Trees and Random Forests. Privacy Enhancing Technologies (PoPETs) 2016, 4 (2016), 335--355. http://dx.doi.org/10.1515/popets-2016-0043

[59]

Andrew Chi-Chih Yao. 1982. Protocols for Secure Computations (Extended Abstract). In Foundations of Computer Science (FOCS'82). IEEE, 160--164.

[60]

Andrew C.-C. Yao. 1986. How to Generate and Exchange Secrets. In Foundations of Computer Science (FOCS'86). IEEE, 162--167.

Digital Library

[61]

Wojciech Zaremba, Ilya Sutskever, and Oriol Vinyals. 2014. Recurrent neural network regularization. CoRR abs/1409.2329 (2014). http://arxiv.org/abs/1409.2329

Cited By

Kluczniak K(2025)Circuit Privacy for FHEW/TFHE-Style Fully Homomorphic Encryption in PracticeIACR Communications in Cryptology10.62056/av11c3w9p1:4Online publication date: 13-Jan-2025
https://doi.org/10.62056/av11c3w9p
Wu YLiao CSun XShen YWu T(2025)Communication Efficient Secure Three-Party Computation Using Lookup Tables for RNN InferenceElectronics10.3390/electronics1405098514:5(985)Online publication date: 28-Feb-2025
https://doi.org/10.3390/electronics14050985
Wei ZSu YZhang XYang HQin JMa J(2025)Machine Learning Meets Encrypted Search: The Impact and Efficiency of OMKSA in Data SecurityInternational Journal of Intelligent Systems10.1155/int/24295772025:1Online publication date: 16-Jan-2025
https://doi.org/10.1155/int/2429577
Show More Cited By

Index Terms

Oblivious Neural Network Predictions via MiniONN Transformations
1. Security and privacy
  1. Security services
    1. Privacy-preserving protocols

Recommendations

Secure Two-Party Computation via Cut-and-Choose Oblivious Transfer

Protocols for secure two-party computation enable a pair of parties to compute a function of their inputs while preserving security properties such as privacy, correctness and independence of inputs. Recently, a number of protocols have been proposed ...
Oblivious Neural Network Computing via Homomorphic Encryption

The problem of secure data processing by means of a neural network (NN) is addressed. Secure processing refers to the possibility that the NN owner does not get any knowledge about the processed data since they are provided to him in encrypted format. ...
Privacy-preserving backpropagation neural network learning

With the development of distributed computing environment, many learning problems now have to deal with distributed input data. To enhance cooperations in learning, it is important to address the privacy concern of each data holder by extending the ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CCS '17: Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security

October 2017

2682 pages

ISBN:9781450349468

DOI:10.1145/3133956

General Chair:
Bhavani Thuraisingham
The University of Texas at Dallas, USA
,
Program Chairs:
David Evans
University of Virginia
,
Tal Malkin
Columbia University
,
Dongyan Xu
Purdue University

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGSAC: ACM Special Interest Group on Security, Audit, and Control

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 30 October 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

The Finnish Funding Agency for Innovation (TEKES)
Academy of Finland

Conference

CCS '17

Sponsor:

SIGSAC

CCS '17: 2017 ACM SIGSAC Conference on Computer and Communications Security

October 30 - November 3, 2017

Texas, Dallas, USA

Acceptance Rates

CCS '17 Paper Acceptance Rate 151 of 836 submissions, 18%;

Overall Acceptance Rate 1,261 of 6,999 submissions, 18%

Upcoming Conference

CCS '25

Sponsor:
sigsac

ACM SIGSAC Conference on Computer and Communications Security

October 13 - 17, 2025

Taipei , Taiwan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

435
Total Citations
View Citations
2,660
Total Downloads

Downloads (Last 12 months)172
Downloads (Last 6 weeks)8

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Kluczniak K(2025)Circuit Privacy for FHEW/TFHE-Style Fully Homomorphic Encryption in PracticeIACR Communications in Cryptology10.62056/av11c3w9p1:4Online publication date: 13-Jan-2025
https://doi.org/10.62056/av11c3w9p
Wu YLiao CSun XShen YWu T(2025)Communication Efficient Secure Three-Party Computation Using Lookup Tables for RNN InferenceElectronics10.3390/electronics1405098514:5(985)Online publication date: 28-Feb-2025
https://doi.org/10.3390/electronics14050985
Wei ZSu YZhang XYang HQin JMa J(2025)Machine Learning Meets Encrypted Search: The Impact and Efficiency of OMKSA in Data SecurityInternational Journal of Intelligent Systems10.1155/int/24295772025:1Online publication date: 16-Jan-2025
https://doi.org/10.1155/int/2429577
Bobo WYang HHao MZhang JHe HZhang W(2025)SEPPDL: A Secure and Efficient Privacy-Preserving Deep Learning Inference Framework for Autonomous DrivingACM Transactions on Autonomous and Adaptive Systems10.1145/3708505Online publication date: 9-Jan-2025
https://doi.org/10.1145/3708505
Feng JWu YSun HZhang SLiu D(2025)Panther: Practical Secure Two-Party Neural Network InferenceIEEE Transactions on Information Forensics and Security10.1109/TIFS.2025.352606320(1149-1162)Online publication date: 2025
https://doi.org/10.1109/TIFS.2025.3526063
Rao BZhang JWu DZhu CSun XChen B(2025)Privacy Inference Attack and Defense in Centralized and Federated Learning: A Comprehensive SurveyIEEE Transactions on Artificial Intelligence10.1109/TAI.2024.33636706:2(333-353)Online publication date: Feb-2025
https://doi.org/10.1109/TAI.2024.3363670
Wang QWang LXu SZhang SShao WMihaljević M(2025)Single-Layer Trainable Neural Network for Secure InferenceIEEE Internet of Things Journal10.1109/JIOT.2024.348019512:3(2968-2978)Online publication date: 1-Feb-2025
https://doi.org/10.1109/JIOT.2024.3480195
Chaudhuri AShukla SBhattacharya SMukhopadhyay D(2025)Secured and Privacy-Preserving GPU-Based Machine Learning Inference in Trusted Execution Environment: A Comprehensive Survey2025 17th International Conference on COMmunication Systems and NETworks (COMSNETS)10.1109/COMSNETS63942.2025.10885734(207-216)Online publication date: 6-Jan-2025
https://doi.org/10.1109/COMSNETS63942.2025.10885734
Hashemi MForte DGanji F(2025)GuardianMPC: Backdoor-Resilient Neural Network ComputationIEEE Access10.1109/ACCESS.2025.352830413(11029-11048)Online publication date: 2025
https://doi.org/10.1109/ACCESS.2025.3528304
Pang ZWang LYu FZhao KZeng BXu S(2025)PrivCore: Multiplication-activation co-reduction for efficient private inferenceNeural Networks10.1016/j.neunet.2025.107307(107307)Online publication date: Mar-2025
https://doi.org/10.1016/j.neunet.2025.107307
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten