The DOI matches its Crossref record; 79 of the 85 references it cites were found in a registry or an index.
CrypTFlow: Secure TensorFlow Inference
Open accessAbstract
We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build three components. Our first component, Athos, is an end-to-end compiler from TensorFlow to a variety of semihonest MPC protocols. The second component, Porthos, is an improved semi-honest 3-party protocol that provides significant speedups for TensorFlow like applications. Finally, to provide malicious secure MPC protocols, our third component, Aramis, is a novel technique that uses hardware with integrity guarantees to convert any semi-honest MPC protocol into an MPC protocol that provides malicious security. The malicious security of the protocols output by Aramis relies on integrity of the hardware and semi-honest security of MPC. Moreover, our system matches the inference accuracy of plaintext TensorFlow.We experimentally demonstrate the power of our system by showing the secure inference of real-world neural networks such as ResNet50 and DenseNet121 over the ImageNet dataset with running times of about 30 seconds for semi-honest security and under two minutes for malicious security. Prior work in the area of secure inference has been limited to semi-honest security of small networks over tiny datasets such as MNIST or CIFAR. Even on MNIST/CIFAR, CrypTFlow outperforms prior work.
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“computational considerations are a concern beyond small algorithm sizes, with efficient SMPC implementations of state-of-the-art neural network algorithms currently under active development 78 .”
From Secure, privacy-preserving and federated machine learning in medical imaging (2020) · cites this paper as [78]
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Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
Densely Connected Convolutional Networks
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate ShiftPreprint
Ioffe, S. (2024) · arXiv (Cornell University) · doi:10.57702/o9raffed · Cited by 15,273
Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46493-0_38 · Cited by 10,741
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., et al. (2016) · arXiv · doi:10.48550/arxiv.1603.04467 · Cited by 9,408
Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
Iandola, F. N., Han, S., Moskewicz, M. W., Ashraf, K., Dally, W. J., & Keutzer, K. (2016) · arXiv · doi:10.48550/arxiv.1602.07360 · Cited by 6,201
Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
Jacob, B., Kligys, S., Chen, B., Zhu, M., Tang, M., Howard, A., Adam, H., & Kalenichenko, D. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00286 · Cited by 4,114
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P., & Zhang, Y. (2017) · 2017 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2017.12 · Cited by 1,936
Exploiting Unintended Feature Leakage in Collaborative Learning
Melis, L., Song, C., De Cristofaro, E., & Shmatikov, V. (2019) · 2019 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp.2019.00029 · Cited by 1,734
Security and Composition of Multiparty Cryptographic Protocols
Canetti, R. (2000) · Journal of Cryptology · doi:10.1007/s001459910006 · Cited by 1,397
(2016) · International Conference on Machine Learning · Cited by 1,270
Efficient Multiparty Protocols Using Circuit Randomization
Beaver, D. (2007) · Springer eBooks · doi:10.1007/3-540-46766-1_34 · Cited by 913
ABY - A Framework for Efficient Mixed-Protocol Secure Two-Party Computation
Demmler, D., Schneider, T. H., & Zohner, M. (2015) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23113 · Cited by 841
Fairplay—a secure two-party computation system
Malkhi, D., Nisan, N., Benny Pinkas, B., & Sella, Y. (2004) · Cited by 791
Oblivious Neural Network Predictions via MiniONN Transformations
Liu, J., Juuti, M., Lu, Y., & Asokan, N. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134056 · Cited by 733
Sharemind: A Framework for Fast Privacy-Preserving Computations
Bogdanov, D., Laur, S., & Willemson, J. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88313-5_13 · Cited by 698
Machine Learning Classification over Encrypted Data
Bost, R., Popa, R. A., Tu, S., & Goldwasser, S. (2015) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2015.23241 · Cited by 683
VC3: Trustworthy Data Analytics in the Cloud Using SGX
Schuster, F., Costa, M., Cédric Fournet, A., Gkantsidis, C., Peinado, M., Mainar-Ruiz, G., & Russinovich, M. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.10 · Cited by 594
MP-SPDZ: A Versatile Framework for Multi-Party Computation
Keller, M. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417872 · Cited by 524
Gazelle: A Low Latency Framework for Secure Neural Network InferencePreprint
Juvekar, C., Vaikuntanathan, V., & Chandrakasan, A. P. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.05507 · Cited by 503
Data-Free Quantization Through Weight Equalization and Bias Correction
Nagel, M., Baalen, M. V., Blankevoort, T., & Welling, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00141 · Cited by 500
Mohassel, P., & Rindal, P. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243760 · Cited by 465
Privacy-Preserving Ridge Regression on Hundreds of Millions of Records
Nikolaenko, V., Weinsberg, U., Ioannidis, S., Jóye, M., Boneh, D., & Taft, N. (2013) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2013.30 · Cited by 447
Riazi, M. S., Weinert, C., Tkachenko, O., Songhori, E. M., Schneider, T. H., & Koushanfar, F. (2018) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3196494.3196522 · Cited by 427
SecureNN: 3-Party Secure Computation for Neural Network Training
Wagh, S., Gupta, D., & Chandran, N. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0035 · Cited by 418
Self-citationHigh-Throughput Semi-Honest Secure Three-Party Computation with an Honest Majority
Araki, T., Furukawa, J., Lindell, Y., Nof, A., & Ohara, K. (2016) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2976749.2978331 · Cited by 413
Oblivious multi-party machine learning on trusted processors
Ohrimenko, O., Schuster, F., Fournet, C., Mehta, A., Nowozin, S., Vaswani, K., & Costa, M. (2016) · MPG.PuRe (Max Planck Society) · Cited by 390
Compilers: Principles, Techniques, and Tools (2nd Edition)
Aho, A. V., Lam, M. S., Sethi, R., & Ullman, J. D. (2006) · Addison-Wesley Longman Publishing Co., Inc. eBooks · Cited by 387
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Tramèr, F., & Boneh, D. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1806.03287 · Cited by 373
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Ryffel, T., Trask, A., Dahl, M., Wagner, B., Mancuso, J., Rueckert, D., & Passerat-Palmbach, J. (2018) · arXiv · doi:10.48550/arxiv.1811.04017 · Cited by 335
ObliVM: A Programming Framework for Secure Computation
Liu, C., Wang, X. S., Nayak, K., Huang, Y., & Shi, E. (2015) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2015.29 · Cited by 309
Privacy-preserving matrix factorization
Nikolaenko, V., Ioannidis, S., Weinsberg, U., Jóye, M., Taft, N., & Boneh, D. (2013) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2508859.2516751 · Cited by 279
CHET: an optimizing compiler for fully-homomorphic neural-network inferencing
Dathathri, R., Saarikivi, O., Chen, H., Laine, K., Lauter, K., Maleki, S., Musuvathi, M., & Mytkowicz, T. (2019) · ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI) · doi:10.1145/3314221.3314628 · Cited by 264
Opaque: an oblivious and encrypted distributed analytics platform
Zheng, W., Dave, A., Beekman, J. G., Popa, R. A., Gonzalez, J. E., & Stoica, I. G. (2017) · Networked Systems Design and Implementation · Cited by 248
Chiron: Privacy-preserving Machine Learning as a ServicePreprint
Hunt, T., Song, C., Shokri, R., Shmatikov, V., & Witchel, E. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.05961 · Cited by 234
Agrawal, N., Shamsabadi, A. S., Kusner, M. J., & Gascón, A. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3339819 · Cited by 229
Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning
Chaudhari, H., Rachuri, R., & Suresh, A. (2020) · Network and Distributed System Security Symposium · doi:10.14722/ndss.2020.23005 · Cited by 221
XONN: XNOR-based Oblivious Deep Neural Network InferencePreprint
Riazi, M. S., Samragh, M., Chen, H., Laine, K., Lauter, K., & Koushanfar, F. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1902.07342 · Cited by 214
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Corrigan-Gibbs, H., & Boneh, D. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.06255 · Cited by 200
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Wang, X. S., Ranellucci, S., & Katz, J. I. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3133979 · Cited by 189
Boemer, F., Costache, A., Cammarota, R., & Wierzynski, C. (2019) · doi:10.1145/3338469.3358944 · Cited by 182
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Authenticated Garbling and Efficient Maliciously Secure Two-Party Computation
Wang, X. S., Ranellucci, S., & Katz, J. I. (2017) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3133956.3134053 · Cited by 167
Boemer, F., Lao, Y., Cammarota, R., & Wierzynski, C. (2019) · ACM International Conference on Computing Frontiers (CF) · doi:10.1145/3310273.3323047 · Cited by 162
Helen: Maliciously Secure Coopetitive Learning for Linear Models
Zheng, W., Popa, R. A., Gonzalez, J. E., & Stoica, I. G. (2019) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2019.00045 · Cited by 150
CodedPrivateML: A Fast and Privacy-Preserving Framework for Distributed Machine Learning
So, J., Güler, B., & Avestimehr, A. S. (2021) · IEEE Journal on Selected Areas in Information Theory · doi:10.1109/jsait.2021.3053220 · Cited by 128
Secure two-party computations in ANSI C
Holzer, A., Franz, M., Katzenbeisser, S., & Veith, H. (2012) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2382196.2382278 · Cited by 125
Wysteria: A Programming Language for Generic, Mixed-Mode Multiparty Computations
Rastogi, A., Hammer, M. A., & Hicks, M. (2014) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp.2014.48 · Cited by 123
Self-citationEzPC: Programmable and Efficient Secure Two-Party Computation for Machine Learning
Chandran, N., Gupta, D., Rastogi, A., Sharma, R., & Tripathi, S. (2019) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2019.00043 · Cited by 122
Self-citationChaudhari, H., Choudhury, A., Patra, A., & Suresh, A. (2019) · Cloud Computing Security Workshop · doi:10.1145/3338466.3358922 · Cited by 104
Choi, S. G., Hwang, K.-W., Katz, J. I., Malkin, T., & Rubenstein, D. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-27954-6_26 · Cited by 102
Compiling KB-sized machine learning models to tiny IoT devices
Gopinath, S., Ghanathe, N., Seshadri, V., & Sharma, R. (2019) · Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation · doi:10.1145/3314221.3314597 · Cited by 92
Self-citationSecure Multiparty Computation from SGX
Bahmani, R., Barbosa, M., Brasser, F., Portela, B., Sadeghi, A., Scerri, G., & Warinschi, B. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-70972-7_27 · Cited by 88
Sealed-Glass Proofs: Using Transparent Enclaves to Prove and Sell Knowledge
Tramèr, F., Zhang, F., Lin, H., Hubaux, J., Juels, A., & Shi, E. (2017) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2017.28 · Cited by 88
Automated Synthesis of Optimized Circuits for Secure Computation
Demmler, D., Dessouky, G., Koushanfar, F., Sadeghi, A., Schneider, T. H., & Zeitouni, S. (2015) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/2810103.2813678 · Cited by 83
Frigate: A Validated, Extensible, and Efficient Compiler and Interpreter for Secure Computation
Mood, B., Gupta, D., Carter, H., Butler, K. R. B., & Traynor, P. (2016) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp.2016.20 · Cited by 80
Self-citationPractical Privacy-Preserving K-means Clustering
Mohassel, P., Rosulek, M., & Trieu, N. (2020) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2020-0080 · Cited by 71
Secure Multi-party Computation
Jajodia, S., Samarati, P., & Yung, M. (2025) · Springer eBooks · doi:10.1007/978-3-030-71522-9_300862 · Cited by 66
Büscher, N., Demmler, D., Katzenbeisser, S., Kretzmer, D., & Schneider, T. H. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243786 · Cited by 65
A domain-specific programming language for secure multiparty computation
Nielsen, J. D., & Schwartzbach, M. I. (2007) · ACM Workshop on Programming Languages and Analysis for Security · doi:10.1145/1255329.1255333 · Cited by 62
Scalable Private Set Union from Symmetric-Key Techniques
Kolesnikov, V., Rosulek, M., Trieu, N., & Wang, X. S. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34621-8_23 · Cited by 58
Optimizing Authenticated Garbling for Faster Secure Two-Party Computation
Katz, J. I., Ranellucci, S., Rosulek, M., & Wang, X. S. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-96878-0_13 · Cited by 54
Generalizing the SPDZ Compiler For Other Protocols
Araki, T., Barak, A., Furukawa, J., Keller, M., Lindell, Y., Ohara, K., & Tsuchida, H. (2018) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3243734.3243854 · Cited by 50
Private Machine Learning in TensorFlow using Secure Computation
Dahl, M., Mancuso, J., Dupis, Y., Decoste, B., Giraud, M., Livingstone, I., Patriquin, J., & Uhma, G. (2018) · arXiv · doi:10.48550/arxiv.1810.08130 · Cited by 48
Using Intel Software Guard Extensions for Efficient Two-Party Secure Function Evaluation
Gupta, D., Mood, B., Joan Feigenbaum, A., Butler, K. R. B., & Traynor, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-662-53357-4_20 · Cited by 37
Self-citationInformation-Flow Control for Programming on Encrypted Data
Mitchell, J. C., Sharma, R., Stefan, D., & Zimmerman, J. (2012) · IEEE Computer Security Foundations Workshop (CSFW) · doi:10.1109/csf.2012.30 · Cited by 34
Self-citationHazay, C., Ishai, Y., Marcedone, A., & Venkitasubramaniam, M. (2019) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3319535.3354258 · Cited by 32
(2019) · IACR Cryptology ePrint Archive · Cited by 30
Secure and Private Function Evaluation with Intel SGX
Felsen, S., Kiss, Á., Schneider, T. H., & Weinert, C. (2019) · Cloud Computing Security Workshop · doi:10.1145/3338466.3358919 · Cited by 26
Ad-Hoc Secure Two-Party Computation on Mobile Devices using Hardware Tokens.Preprint
Demmler, D., Schneider, T. H., & Zohner, M. (2014) · IACR Cryptology ePrint Archive · Cited by 23
A Hybrid Approach to Secure Function Evaluation using SGX
Choi, J. I., Tian, D. (., Hernandez, G., Patton, C., Mood, B., Shrimpton, T., Butler, K. R. B., & Traynor, P. (2019) · ACM Asia Conference on Computer and Communications Security (ASIA CCS) · doi:10.1145/3321705.3329835 · Cited by 19
Secure Two-Party Computation: A Visual Way
D’Arco, P., & Prisco, R. D. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-04268-8_2 · Cited by 16
Time to Rethink: Trust Brokerage Using Trusted Execution Environments
Koeberl, P., Phegade, V., Rajan, A., Schneider, T. H., Schulz, S., & Zhdanova, M. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-22846-4_11 · Cited by 15
Secure Evaluation of Quantized Neural Networks.Preprint
Barak, A., Escudero, D. E., Dalskov, A., & Keller, M. (2019) · IACR Cryptology ePrint Archive · Cited by 15
Outsourcing Private Machine Learning via Lightweight Secure Arithmetic ComputationPreprint
Garg, S., Ghodsi, Z., Hazay, C., Ishai, Y., Marcedone, A., & Venkitasubramaniam, M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1812.01372 · Cited by 4
Research works printed but not linked: 5
- goldreich, 1987, How to Play any Mental Game or A Completeness Theorem for Protocols with Honest Majority, 19th Annual ACM Symposium on Theory of Computing 1987, 218
- msv, 2017, Google’s Research In Artificial Intelligence Helps In Preventing Blindness Caused by Diabetes, Forbes
- 10.1145/3338466.3358922 · DOI
- yao, 1986, How to Generate and Exchange Secrets (Extended Abstract), 27th Annual Symposium on Foundations of Computer Science, 162
- aho, 2006, Compilers Principles Techniques and Tools
Cited by
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Trustworthy AI: From Principles to Practices
Li, B., Qi, P., Liu, B., Di, S., Liu, J., Pei, J., Yi, J., & Zhou, B. (2023) · ACM Computing Surveys · doi:10.1145/3555803 · Cited by 612
“Several tools have been open sourced, such as MP2ML [48], CryptoSPN [330], CrypTFlow [200, 276], and CrypTen [188].”
MethodCrypTFlow2: Practical 2-Party Secure Inference
Rathee, D., Rathee, M., Kumar, N., Chandran, N., Gupta, D., Rastogi, A., & Sharma, R. (2020) · ACM SIGSAC Conference on Computer and Communications Security (CCS) · doi:10.1145/3372297.3417274 · Cited by 354
Self-citationRahman, M. A., Hossain, M. S., Islam, M. S., Alrajeh, N. A., & Muhammad, G. (2020) · IEEE Access · doi:10.1109/access.2020.3037474 · Cited by 312
CrypTen: Secure Multi-Party Computation Meets Machine Learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., & van der Maaten, L. (2021) · arXiv · doi:10.48550/arxiv.2109.00984 · Cited by 257
Metadata
- Authors
- Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., & Sharma, R.
- Affiliations
- Microsoft (United States) (US)
- Venue
- 2020 IEEE Symposium on Security and Privacy (SP)
- Publisher
- IEEE
- Published
- 2020-05
- Volume
- 2019
- Pages
- 336-353
- ISSN
- 1081-6011 (ISSN-L)
- OpenAlex
- W2974824775
- Semantic Scholar
- 5a2a7bcb24488a4ee938e376cc876d7c2fe115a7
- Cited by
- 245
- References
- 85
- Citations by year
- 2026: 18 · 2025: 41 · 2024: 48 · 2023: 43 · 2022: 33 · 2021: 42 · 2020: 20
- FWCI
- 13.18
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- IEEE Symposium on Security and Privacy (S&P) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- IACR Cryptology ePrint Archive (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Networks and Communications › Cryptography and Data Security
- Physical Sciences › Computer Science › Hardware and Architecture › Security and Verification in Computing
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- secure multi-party computation, secure inference, TensorFlow, neural network inference, semi-honest security, malicious security, compilers, hardware integrity, secure machine learning, ResNet50, DenseNet121, ImageNet
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