Fetching the paper…
Reading the bibliography…
Recently, private inference (PI) has addressed the rising concern over data and model privacy in machine learning inference as a service.
Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE transactions on image processing
2004
Earlier work this paper cites.
A. Krizhevsky, G. Hinton, et al
2009
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” Advances in neural information processing systems
2012
Earlier work this paper cites.
2014
Earlier work this paper cites.
R. Gilad-Bachrach, N. Dowlin, K. Laine, K. Lauter, M. Naehrig, and J. Wernsing, “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in International conference on machine learning
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition
2016
Earlier work this paper cites.
J. Liu, M. Juuti, Y. Lu, and N. Asokan, “Oblivious neural network predictions via minionn transformations,” in Proceedings of the 2017 ACM SIGSAC conference on computer and communications security
2017
Earlier work this paper cites.
C. Juvekar, V. Vaikuntanathan, and A. Chandrakasan, “ { \{ GAZELLE } \} : A low latency framework for secure neural network inference,” in 27th USENIX Security Symposium (USENIX Security 18)
2018
Earlier work this paper cites.
M. S. Riazi, M. Samragh, H. Chen, K. Laine, K. Lauter, and F. Koushanfar, “ { \{ XONN } \} : { \{ XNOR-based } \} oblivious deep neural network inference,” in 28th USENIX Security Symposium (USENIX Security 19)
2019
Earlier work this paper cites.
Z. He, T. Zhang, and R. B. Lee, “Model inversion attacks against collaborative inference,” in Proceedings of the 35th Annual Computer Security Applications Conference
2019
Earlier work this paper cites.
P. Mishra, R. Lehmkuhl, A. Srinivasan, W. Zheng, and R. A. Popa, “Delphi: A cryptographic inference service for neural networks,” in 29th USENIX Security Symposium (USENIX Security 20)
2020
Earlier work this paper cites.
D. Rathee, M. Rathee, N. Kumar, N. Chandran, D. Gupta, A. Rastogi, and R. Sharma, “Cryptflow2: Practical 2-party secure inference,” in Proceedings of the 2020 ACM SIGSAC Conference on CCS
2020
Cited alongside, same era.
Z. Liu, Z. Wu, C. Gan, L. Zhu, and S. Han, “Datamix: Efficient privacy-preserving edge-cloud inference,” in European Conference on Computer Vision
2020
Cited alongside, same era.
S. Kundu, Q. Sun, Y. Fu, M. Pedram, and P. Beerel, “Analyzing the confidentiality of undistillable teachers in knowledge distillation,” Advances in Neural Information Processing Systems
2021
Cited alongside, same era.
S. Tan, B. Knott, Y. Tian, and D. J. Wu, “Cryptgpu: Fast privacy-preserving machine learning on the gpu,” in 2021 IEEE Symposium on Security and Privacy (SP)
2021
Cited alongside, same era.
2021
Later among the works it cites.
S. Kundu and S. Sundaresan, “Attentionlite: Towards efficient self-attention models for vision,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2021
Later among the works it cites.
Z. Huang, W. jie Lu, C. Hong, and J. Ding, “Cheetah: Lean and fast secure two-party deep neural network inference.” Cryptology ePrint Archive, Paper 2022/207
2022
Later among the works it cites.
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2021
Cited alongside, same era.
R. Lehmkuhl, P. Mishra, A. Srinivasan, and R. A. Popa, “Muse: Secure inference resilient to malicious clients,” in 30th USENIX Security Symposium (USENIX Security 21)
2021
Cited alongside, same era.
Y. Zhang, R. Yasaei, H. Chen, Z. Li, and M. A. Al Faruque, “Stealing neural network structure through remote fpga side-channel analysis,” IEEE Transactions on Information Forensics and Security
2021
Cited alongside, same era.
Z. Ghodsi, N. K. Jha, B. Reagen, and S. Garg, “Circa: Stochastic relus for private deep learning,” Advances in Neural Information Processing Systems
2021
Cited alongside, same era.
N. Chandran, D. Gupta, S. L. B. Obbattu, and A. Shah, “Simc: Ml inference secure against malicious clients at semi-honest cost,” Cryptology ePrint Archive
2021
Cited alongside, same era.
S. Maji, U. Banerjee, and A. P. Chandrakasan, “Leaky nets: Recovering embedded neural network models and inputs through simple power and timing side-channels—attacks and defenses,” IEEE Internet of Things Journal
2021
Cited alongside, same era.
L. Shen, Y. Dong, B. Fang, J. Shi, X. Wang, S. Pan, and R. Shi, “Abnn2: secure two-party arbitrary-bitwidth quantized neural network predictions,” in Proc. of the 59th ACM/IEEE Design Automation Conference
2022
Later among the works it cites.
J. Li, A. S. Rakin, X. Chen, Z. He, D. Fan, and C. Chakrabarti, “Ressfl: A resistance transfer framework for defending model inversion attack in split federated learning,” in Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition
2022
Later among the works it cites.
M. Cho, A. Joshi, B. Reagen, S. Garg, and C. Hegde, “Selective network linearization for efficient private inference,” in International Conference on Machine Learning
2022
Later among the works it cites.
2022
Later among the works it cites.
S. Kundu, Y. Zhang, D. Chen, and P. A. Beerel, “Making models shallow again: Jointly learning to reduce non-linearity and depth for latency-efficient private inference,” in 2023 CVPR Workshop
2023
Closest in time.