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Performing neural network inference on encrypted data without decryption is one popular method to enable privacy-preserving neural networks (PNet) as a service.
AdvMind: Inferring adversary intent of black-box attacks
Pang, R.; Zhang, X.; Ji, S.; Luo, X.; and Wang, T. 2020 · 1907
Earlier work this paper cites.
Sign-opt: A query-efficient hard-label adversarial attack
Cheng, M.; Singh, S.; Chen, P.; Chen, P.-Y.; Liu, S.; and Hsieh, C.-J. 2019 · 1909
Earlier work this paper cites.
Blacklight: Defending black-box adversarial attacks on deep neural networks
Li, H.; Shan, S.; Wenger, E.; Zhang, J.; Zheng, H.; and Zhao, B. Y. 2020 · 2006
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MNIST Handwritten Digit Database
LeCun, Y.; Cortes, C.; and Burges, C. 2010 · 2010
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Explaining and harnessing adversarial examples
Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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The CIFAR-10 dataset
Krizhevsky, A.; Nair, V.; and Hinton, G. 2014 · 2014
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On the concrete hardness of Learning with Errors
Albrecht, M. R.; Player, R.; and Scott, S. 2015 · 2015
Earlier work this paper cites.
CryptoNets: Applying Neural Networks to Encrypted Data with High Throughput and Accuracy
Dowlin, N.; et al. 2016 · 2016
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs
Gulshan, V.; et al. 2016 · 2016
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Faster homomorphic evaluation of discrete fourier transforms
Costache, A.; Smart, N. P.; and Vivek, S. 2017 · 2017
Earlier work this paper cites.
SecureML: A System for Scalable Privacy-Preserving Machine Learning
Mohassel, P.; and Zhang, Y. 2017 · 2017
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Mitigating adversarial effects through randomization
Xie, C.; Wang, J.; Zhang, Z.; Ren, Z.; and Yuille, A. 2017 · 2017
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Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Chou, E.; et al. 2018 · 2018
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Adversarial examples that fool both computer vision and time-limited humans
Elsayed, G.; Shankar, S.; Cheung, B.; Papernot, N.; Kurakin, A.; Goodfellow, I.; and Sohl-Dickstein, J. 2018 · 2018
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Black-box adversarial attacks with limited queries and information
Ilyas, A.; Engstrom, L.; Athalye, A.; and Lin, J. 2018 · 2018
Cited alongside, same era.
Prior convictions: Black-box adversarial attacks with bandits and priors
Ilyas, A.; Engstrom, L.; and Madry, A. 2018 · 2018
Cited alongside, same era.
GAZELLE: A Low Latency Framework for Secure Neural Network Inference
Juvekar, C.; et al. 2018 · 2018
Cited alongside, same era.
Low Latency Privacy Preserving Inference
Brutzkus, A.; et al. 2019 · 2019
Cited alongside, same era.
Certified adversarial robustness via randomized smoothing
Cohen, J.; Rosenfeld, E.; and Kolter, Z. 2019 · 2019
Cited alongside, same era.
Simple black-box adversarial attacks
Guo, C.; Gardner, J.; You, Y.; Wilson, A. G.; and Weinberger, K. 2019 · 2019
Delphi: A Cryptographic Inference Service for Neural Networks
Mishra, P.; Lehmkuhl, R.; Srinivasan, A.; Zheng, W.; and Popa, R. A. 2020 · 2020
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A simple way to make neural networks robust against diverse image corruptions
Rusak, E.; Schott, L.; Zimmermann, R. S.; Bitterwolf, J.; Bringmann, O.; Bethge, M.; and Brendel, W. 2020 · 2020
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Denoised smoothing: A provable defense for pretrained classifiers
Salman, H.; Sun, M.; Yang, G.; Kapoor, A.; and Kolter, J. Z. 2020 · 2020
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On Adaptive Attacks to Adversarial Example Defenses
Tramer, F.; Carlini, N.; Brendel, W.; and Madry, A. 2020 · 2020
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Small input noise is enough to defend against query-based black-box attacks
Byun, J.; Go, H.; and Kim, C. 2021 · 2021
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Cited alongside, same era.
Parametric noise injection: Trainable randomness to improve deep neural network robustness against adversarial attack
He, Z.; Rakin, A. S.; and Fan, D. 2019 · 2019
Cited alongside, same era.
Parsimonious black-box adversarial attacks via efficient combinatorial optimization
Moon, S.; An, G.; and Song, H. O. 2019 · 2019
Cited alongside, same era.
Provably robust deep learning via adversarially trained smoothed classifiers
Salman, H.; Li, J.; Razenshteyn, I.; Zhang, P.; Zhang, H.; Bubeck, S.; and Yang, G. 2019 · 2019
Cited alongside, same era.
Microsoft SEAL (release 3.4)
SEAL. 2019 · 2019
Cited alongside, same era.
Sign Bits Are All You Need for Black-Box Attacks
Al-Dujaili, A.; and O’Reilly, U.-M. 2020 · 2020
Cited alongside, same era.
Stateful detection of black-box adversarial attacks
Chen, S.; Carlini, N.; and Wagner, D. 2020 · 2020
Cited alongside, same era.
Cape Privacy: Privacy & trust management for machine learning
CapePrivacy. 2021 · 2021
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SAFENet: A Secure, Accurate and Fast Neural Network Inference
Lou, Q.; Shen, Y.; Jin, H.; and Jiang, L. 2021 · 2021
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Random noise defense against query-based black-box attacks
Qin, Z.; Fan, Y.; Zha, H.; and Wu, B. 2021 · 2021
Later among the works it cites.
DualityTechnologies: Data encryption technology and secure collaboration
DualityTechnologies. 2022 · 2022
Closest in time.
Boosting black-box attack with partially transferred conditional adversarial distribution
Feng, Y.; Wu, B.; Fan, Y.; Liu, L.; Li, Z.; and Xia, S.-T. 2022 · 2022
Closest in time.
Patch-Fool: Are Vision Transformers Always Robust Against Adversarial Perturbations?
Fu, Y.; Zhang, S.; Wu, S.; Wan, C.; and Lin, Y. 2022 · 2022
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Inpher: Secret computing and privacy-preserving analytics
Inpher. 2022 · 2022
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Concrete ML
Zama. 2022 · 2022
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