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This work presents Origami, which provides privacy-preserving inference for large deep neural network (DNN) models through a combination of enclave execution, cryptographic blinding, interspersed with accelerator-based computation.
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2015
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2018
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C. Juvekar, V. Vaikuntanathan, and A. Chandrakasan, “Gazelle: A low latency framework for secure neural network inference,” in Proceedings of the 27th USENIX Conference on Security Symposium , ser. SEC’18. Berkeley, CA, USA: USENIX Association, 2018, pp. 1651–1668. [Online]. Available: http://dl.acm.org/citation.cfm?id=3277203.3277326
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2018
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2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , June 2016, pp. 770–778
2016
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2017
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Y. Kang, J. Hauswald, C. Gao, A. Rovinski, T. Mudge, J. Mars, and L. Tang, “Neurosurgeon: Collaborative intelligence between the cloud and mobile edge,” in Proceedings of the Twenty-Second International Conference on Architectural Support for Programming Languages and Operating Systems , ser. ASPLOS ’17. New York, NY, USA: ACM, 2017, pp. 615–629. [Online]. Available: http://doi.acm.org/10.1145/3037697.3037698
2017
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2017
Cited alongside, same era.
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 , ser. CCS ’17. New York, NY, USA: ACM, 2017, pp. 619–631. [Online]. Available: http://doi.acm.org/10.1145/3133956.3134056
2017
Cited alongside, same era.
L. Phong, Y. Aono, T. Hayashi, L. Wang, and S. Moriai, “Privacy-preserving deep learning via additively homomorphic encryption,” IEEE Transactions on Information Forensics and Security , vol. PP, pp. 1–1, 12 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
J. V. Bulck, M. Minkin, O. Weisse, D. Genkin, B. Kasikci, F. Piessens, M. Silberstein, T. F. Wenisch, Y. Yarom, and R. Strackx, “Foreshadow: Extracting the keys to the intel SGX kingdom with transient out-of-order execution,” in 27th USENIX Security Symposium (USENIX Security 18) . Baltimore, MD: USENIX Association, Aug. 2018, p. 991–1008. [Online]. Available: https://www.usenix.org/conference/usenixsecurity18/presentation/bulck
2018
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2018
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2018
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M. Yan, J. Choi, D. Skarlatos, A. Morrison, C. Fletcher, and J. Torrellas, “Invisispec: Making speculative execution invisible in the cache hierarchy,” in 2018 51st Annual IEEE/ACM International Symposium on Microarchitecture (MICRO) , Oct 2018, pp. 428–441
2018
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G. Chen, S. Chen, Y. Xiao, Y. Zhang, Z. Lin, and T. H. Lai, “Sgxpectre: Stealing intel secrets from sgx enclaves via speculative execution,” 2019 IEEE European Symposium on Security and Privacy , Jun 2019. [Online]. Available: http://dx.doi.org/10.1109/EuroSP.2019.00020
2019
Closest in time.
Intel, “Intel software guard extensions sdk for linux,” 2019. [Online]. Available: https://01.org/sites/default/files/documentation/intel_sgx_sdk_developer_reference_for_linux_os_pdf
2019
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P. Kocher, J. Horn, A. Fogh, D. Genkin, D. Gruss, W. Haas, M. Hamburg, M. Lipp, S. Mangard, T. Prescher, and et al., “Spectre attacks: Exploiting speculative execution,” 2019 IEEE Symposium on Security and Privacy (SP) , May 2019. [Online]. Available: http://dx.doi.org/10.1109/SP.2019.00002
2019
Closest in time.
F. Mireshghallah, M. Taram, P. Ramrakhyani, D. Tullsen, and H. Esmaeilzadeh, “Shredder: Learning noise distributions to protect inference privacy,” 2019
2019
Closest in time.
J. Yu, M. Yan, A. Khyzha, A. Morrison, J. Torrellas, and C. W. Fletcher, “Speculative taint tracking (stt): A comprehensive protection for speculatively accessed data,” in Proceedings of the 52Nd Annual IEEE/ACM International Symposium on Microarchitecture , ser. MICRO ’52. New York, NY, USA: ACM, 2019, pp. 954–968. [Online]. Available: http://doi.acm.org/10.1145/3352460.3358274
2019
Closest in time.