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PPDL - Privacy Preserving Deep Learning Using Homomorphic Encryption

Published: 08 January 2022 Publication History

Abstract

Deep Learning Models such as Convolution Neural Networks (CNNs) have shown great potential in various applications. However, these techniques will face regulatory compliance challenges related to privacy of user data, especially when they are deployed as a service on a cloud platform. Such concerns can be mitigated by using privacy preserving machine learning techniques. The purpose of our work is to explore a class of privacy preserving machine learning technique called Fully Homomorphic Encryption in enabling CNN inference on encrypted real-world dataset. Fully homomorphic encryption face the limitation of computational depth. They are also resource intensive operations. We run our experiments on MNIST dataset to understand the challenges and identify the optimization techniques. We used these insights to achieve the end goal of enabling encrypted inference for binary classification on melanoma dataset using Cheon-Kim-Kim-Song (CKKS) encryption scheme available in the open-source HElib library.

References

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Alon Brutzkus, Ran Gilad-Bachrach, and Oren Elisha. 2019. Low latency privacy preserving inference. In International Conference on Machine Learning. 812–821.
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Edward Chou, Josh Beal, Daniel Levy, Serena Yeung, Albert Haque, and Li Fei-Fei. 2018. Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference. CoRR abs/1811.09953(2018). arxiv:1811.09953http://arxiv.org/abs/1811.09953
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Nathan Dowlin, Ran Gilad-Bachrach, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. 2016. Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy. In International Conference on Machine Learning. 201–210.
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Zahra Ghodsi, Akshaj Veldanda, Brandon Reagen, and Siddharth Garg. 2021. CryptoNAS: Private Inference on a ReLU Budget. arxiv:2006.08733 [cs.LG]
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David A. Gutman, Noel C. F. Codella, M. Emre Celebi, Brian Helba, Michael A. Marchetti, Nabin K. Mishra, and Allan Halpern. 2016. Skin Lesion Analysis toward Melanoma Detection: A Challenge at the International Symposium on Biomedical Imaging (ISBI) 2016, hosted by the International Skin Imaging Collaboration (ISIC). CoRR abs/1605.01397(2016). arXiv:1605.01397http://arxiv.org/abs/1605.01397
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Takumi Ishiyama, Takuya Suzuki, and Hayato Yamana. 2020. Highly Accurate CNN Inference Using Approximate Activation Functions over Homomorphic Encryption. arxiv:2009.03727 [cs.LG]
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Nandan Kumar Jha, Zahra Ghodsi, Siddharth Garg, and Brandon Reagen. 2021. DeepReDuce: ReLU Reduction for Fast Private Inference. arxiv:2103.01396 [cs.LG]
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Chiraag Juvekar, Vinod Vaikuntanathan, and Anantha Chandrakasan. 2018. GAZELLE: A low latency framework for secure neural network inference. In 27th USENIX Security Symposium (USENIX Security 18). 1651–1669.
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Guillermo Lloret-Talavera, Marc Jorda, Harald Servat, Fabian Boemer, Chetan Chauhan, Shigeki Tomishima, Nilesh N. Shah, and Antonio J Pena. 2021. Enabling Homomorphically Encrypted Inference for Large DNN Models. IEEE Trans. Comput. (2021), 1–1. https://doi.org/10.1109/tc.2021.3076123
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Qian Lou and Lei Jiang. 2019. SHE: A Fast and Accurate Deep Neural Network for Encrypted Data. In Advances in Neural Information Processing Systems. 10035–10043.
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Qian Lou and Lei Jiang. 2021. HEMET: A Homomorphic-Encryption-Friendly Privacy-Preserving Mobile Neural Network Architecture. arxiv:2106.00038 [cs.CR]

Cited By

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  • (2023)Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191274(1-7)Online publication date: 18-Jun-2023
  • (2023)Enabling All In-Edge Deep Learning: A Literature ReviewIEEE Access10.1109/ACCESS.2023.323476111(3431-3460)Online publication date: 2023
  • (2023)Privacy-preserving artificial intelligence in healthcareComputers in Biology and Medicine10.1016/j.compbiomed.2023.106848158:COnline publication date: 1-May-2023

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  1. PPDL - Privacy Preserving Deep Learning Using Homomorphic Encryption
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            cover image ACM Conferences
            CODS-COMAD '22: Proceedings of the 5th Joint International Conference on Data Science & Management of Data (9th ACM IKDD CODS and 27th COMAD)
            January 2022
            357 pages
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            Publication History

            Published: 08 January 2022

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            Author Tags

            1. Convolutional neural network
            2. ciphertext packing
            3. homomorphic encryption
            4. multi-threading
            5. non-linear activation function
            6. optimization

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            View all
            • (2023)Implementing Responsible AI: Tensions and Trade-Offs Between Ethics Aspects2023 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN54540.2023.10191274(1-7)Online publication date: 18-Jun-2023
            • (2023)Enabling All In-Edge Deep Learning: A Literature ReviewIEEE Access10.1109/ACCESS.2023.323476111(3431-3460)Online publication date: 2023
            • (2023)Privacy-preserving artificial intelligence in healthcareComputers in Biology and Medicine10.1016/j.compbiomed.2023.106848158:COnline publication date: 1-May-2023

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