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As ML models have increased in capabilities and accuracy, so has the complexity of their deployments.
The use of confidence or fiducial limits illustrated in the case of the binomial
Clopper, C. J. and Pearson, E. S · 1934
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A method for obtaining digital signatures and public-key cryptosystems
Rivest, R. L., Shamir, A., and Adleman, L · 1978
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Cryptographic hash-function basics: Definitions, implications, and separations for preimage resistance, second-preimage resistance, and collision resistance
Rogaway, P. and Shrimpton, T · 2004
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Quadratic span programs and succinct nizks without pcps
Gennaro, R., Gentry, C., Parno, B., and Raykova, M · 2013
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Time-optimal interactive proofs for circuit evaluation
Thaler, J · 2013
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A guide to fully homomorphic encryption
Armknecht, F., Boyd, C., Carr, C., Gjøsteen, K., Jäschke, A., Reuter, C. A., and Strand, M · 2015
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Interactive oracle proofs
Ben-Sasson, E., Chiesa, A., and Spooner, N · 2016
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On the size of pairing-based non-interactive arguments
Groth, J · 2016
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The hunting of the snark
Bitansky, N., Canetti, R., Chiesa, A., Goldwasser, S., Lin, H., Rubinstein, A., and Tromer, E · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Mohassel, P. and Zhang, Y · 2017
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Scalable, transparent, and post-quantum secure computational integrity
Ben-Sasson, E., Bentov, I., Horesh, Y., and Riabzev, M · 2018
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{ \{ GAZELLE } \} : A low latency framework for secure neural network inference
Juvekar, C., Vaikuntanathan, V., and Chandrakasan, A · 2018
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Mobilenetv2: Inverted residuals and linear bottlenecks
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C · 2018
Cited alongside, same era.
Tf-slim: A high level library to define complex models in tensorflow, 2018
Silberman, N. and Guadarrama, S · 2018
Cited alongside, same era.
Recursive proof composition without a trusted setup
Bowe, S., Grigg, J., and Hopwood, D · 2019
Cited alongside, same era.
Plonk: Permutations over lagrange-bases for oecumenical noninteractive arguments of knowledge
Gabizon, A., Williamson, Z. J., and Ciobotaru, O · 2019
Cited alongside, same era.
Poseidon: A new hash function for zero-knowledge proof systems
Grassi, L., Khovratovich, D., Rechberger, C., Roy, A., and Schofnegger, M · 2019
Cited alongside, same era.
The cost of machine learning projects
Incze, R · 2019
Cited alongside, same era.
From airs to raps - how plonk-style arithmetization works
Gabizon, A · 2021
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Deepreduce: Relu reduction for fast private inference
Jha, N. K., Ghodsi, Z., Garg, S., and Reagen, B · 2021
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Crypten: Secure multi-party computation meets machine learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L · 2021
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Zkcnn: Zero knowledge proofs for convolutional neural network predictions and accuracy
Liu, T., Xie, X., and Zhang, Y · 2021
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Hemet: A homomorphic-encryption-friendly privacy-preserving mobile neural network architecture
Lou, Q. and Jiang, L · 2021
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Privacy-preserving video classification with convolutional neural networks
Pentyala, S., Dowsley, R., and De Cock, M · 2021
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Transparent snarks from dark compilers
Bünz, B., Fisch, B., and Szepieniec, A · 2020
Cited alongside, same era.
plookup: A simplified polynomial protocol for lookup tables
Gabizon, A. and Williamson, Z. J · 2020
Cited alongside, same era.
Cryptflow: Secure tensorflow inference
Kumar, N., Rathee, M., Chandran, N., Gupta, D., Rastogi, A., and Sharma, R · 2020
Cited alongside, same era.
vcnn: Verifiable convolutional neural network based on zk-snarks
Lee, S., Ko, H., Kim, J., and Oh, H · 2020
Cited alongside, same era.
Delphi: A cryptographic inference service for neural networks
Mishra, P., Lehmkuhl, R., Srinivasan, A., Zheng, W., and Popa, R. A · 2020
Cited alongside, same era.
Zen: An optimizing compiler for verifiable, zero-knowledge neural network inferences
Feng, B., Qin, L., Zhang, Z., Ding, Y., and Chu, S · 2021
Cited alongside, same era.
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Tabula: Efficiently computing nonlinear activation functions for secure neural network inference
Lam, M., Mitzenmacher, M., Reddi, V. J., Wei, G.-Y., and Brooks, D · 2022
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Plonkup: Reconciling plonk with plookup
Pearson, L., Fitzgerald, J., Masip, H., Bellés-Muñoz, M., and Muñoz-Tapia, J. L · 2022
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zkevm, 2022
Privacy and Explorations, S · 2022
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pvcnn: Privacy-preserving and verifiable convolutional neural network testing
Weng, J., Weng, J., Tang, G., Yang, A., Li, M., and Liu, J.-N · 2022
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halo2, 2022
zcash · 2022
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