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There is an increasing conflict between business incentives to hide models and data as trade secrets, and the societal need for algorithmic transparency.
A digital signature based on a conventional encryption function
Merkle, R. C · 1988
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Random oracles are practical: A paradigm for designing efficient protocols
Bellare, M. and Rogaway, P · 1993
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Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
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Constant-size commitments to polynomials and their applications
Kate, A., Zaverucha, G. M., and Goldberg, I · 2010
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3d object representations for fine-grained categorization
Krause, J., Stark, M., Deng, J., and Fei-Fei, L · 2013
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The movielens datasets: History and context
Harper, F. M. and Konstan, J. A · 2015
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Accountable algorithms
Kroll, J. A · 2015
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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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Understanding and optimizing asynchronous low-precision stochastic gradient descent
De Sa, C., Feldman, M., Ré, C., and Olukotun, K · 2017
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Safetynets: Verifiable execution of deep neural networks on an untrusted cloud
Ghodsi, Z., Gu, T., and Garg, S · 2017
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Secureml: A system for scalable privacy-preserving machine learning
Mohassel, P. and Zhang, Y · 2017
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Certified defenses for data poisoning attacks
Steinhardt, J., Koh, P. W. W., and Liang, P. S · 2017
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vsql: Verifying arbitrary sql queries over dynamic outsourced databases
Zhang, Y., Genkin, D., Katz, J., Papadopoulos, D., and Papamanthou, C · 2017
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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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Handbook of applied cryptography
Menezes, A. J., Van Oorschot, P. C., and Vanstone, S. A · 2018
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Simulation extractability in groth’s zk-snark
Atapoor, S. and Baghery, K · 2019
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Plonk: Permutations over lagrange-bases for oecumenical noninteractive arguments of knowledge
Gabizon, A., Williamson, Z. J., and Ciobotaru, O · 2019
Cited alongside, same era.
Deep learning recommendation model for personalization and recommendation systems
Naumov, M., Mudigere, D., Shi, H.-J. M., Huang, J., Sundaraman, N., Park, J., Wang, X., Gupta, U., Wu, C.-J., Azzolini, A. G., et al · 2019
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.
Lecture 3: Concentration inequalities and mean estimation, 2020
Lee, J · 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.
Nova: Recursive zero-knowledge arguments from folding schemes
Kothapalli, A., Setty, S., and Tzialla, I · 2022
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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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Confidential-profitt: Confidential proof of fair training of trees
Shamsabadi, A. S., Wyllie, S. C., Franzese, N., Dullerud, N., Gambs, S., Papernot, N., Wang, X., and Weller, A · 2022
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Proofs, arguments, and zero-knowledge
Thaler, J. et al · 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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Shanthi, T., Sabeenian, R., and Anand, R · 2020
Cited alongside, same era.
Proofs for inner pairing products and applications
Bünz, B., Maller, M., Mishra, P., Tyagi, N., and Vesely, P · 2021
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.
Crypten: Secure multi-party computation meets machine learning
Knott, B., Venkataraman, S., Hannun, A., Sengupta, S., Ibrahim, M., and van der Maaten, L · 2021
Cited alongside, same era.
Zkcnn: Zero knowledge proofs for convolutional neural network predictions and accuracy
Liu, T., Xie, X., and Zhang, Y · 2021
Cited alongside, same era.
Hemet: A homomorphic-encryption-friendly privacy-preserving mobile neural network architecture
Lou, Q. and Jiang, L · 2021
Cited alongside, same era.
Privacy-preserving video classification with convolutional neural networks
Pentyala, S., Dowsley, R., and De Cock, M · 2021
Cited alongside, same era.
halo2, 2022
zcash · 2022
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Counterfactual metrics for auditing black-box recommender systems for ethical concerns
Akpinar, N.-J., Leqi, L., Hadfield-Menell, D., and Lipton, Z · 2023
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Jolt: Snarks for virtual machines via lookups
Arun, A., Setty, S., and Thaler, J · 2023
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What did twitter’s ‘open source’ algorithm actually reveal? not a lot
Bell, K · 2023
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Experimenting with zero-knowledge proofs of training
Garg, S., Goel, A., Jha, S., Mahloujifar, S., Mahmoody, M., Policharla, G.-V., and Wang, M · 2023
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It’s time to reveal all recommendation algorithms – by law if necessary
Pesce, M · 2023
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Perpetual powers of tau, 2023
PSE · 2023
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Secure floating-point training
Rathee, D., Bhattacharya, A., Gupta, D., Sharma, R., and Song, D · 2023
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Zkdl: Efficient zero-knowledge proofs of deep learning training
Sun, H., Bai, T., Li, J., and Zhang, H · 2023
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Twitter’s recommendation algorithm
Twitter · 2023
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Zero-knowledge proofs of training for deep neural networks
Abbaszadeh, K., Pappas, C., Papadopoulos, D., and Katz, J · 2024
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