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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭斯彥 | zh_TW |
| dc.contributor.advisor | Sy-Yen Kuo | en |
| dc.contributor.author | 連冠棨 | zh_TW |
| dc.contributor.author | Guan-Ci Lien | en |
| dc.date.accessioned | 2024-07-30T16:11:03Z | - |
| dc.date.available | 2024-07-31 | - |
| dc.date.copyright | 2024-07-30 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-27 | - |
| dc.identifier.citation | CIFAR-10 (canadian institute for advanced research). Available online: https://www.cs.toronto.edu/~kriz/cifar.html. Accessed: 2024-01-20.
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Cryptology ePrint Archive, Report 2012/144, 2012. https://eprint.iacr.org/2012/144. C. Gentry. Fully homomorphic encryption using ideal lattices. In Proceedings of the 41st Annual ACM Symposium on Theory of Computing, STOC ’09. ACM, 2009. C. Gentry, A. Sahai, and B. Waters. Homomorphic encryption from learning with errors: Conceptually-simpler, asymptotically-faster, attribute-based. Cryptology ePrint Archive, Paper 2013/340, 2013. https://eprint.iacr.org/2013/340. X. Glorot, A. Bordes, and Y. Bengio. Deep sparse rectifier neural networks.In Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, volume 15 of AISTATS ’11, pages 315–323, Apr 2011. G. Jagannathan, K. Pillaipakkamnatt, and R. N. Wright. A practical differentially private random decision tree classifier. In 2009 IEEE International Conference on Data Mining Workshops, pages 114–121, 2009. H. Kim, K. E. Kim, S. Park, and J. Sohn. E-voting system using homomorphic encryption and blockchain technology to encrypt voter data. CoRR, abs/2111.05096, 2021. M. M. Lau and K. H. Lim. Review of adaptive activation function in deep neural network. In Proceedings of the IEEE-EMBS Conference on Biomedical Engineering and Sciences, pages 686–690, Dec 2018. J. Lee, H. Kang, Y. Lee, W. Choi, J. Eom, M. Deryabin, E. Lee, J. Lee, D. Yoo, Y. Kim, and J. No. Privacy-preserving machine learning with fully homomorphic encryption for deep neural network. CoRR, abs/2106.07229, 2021. F.-F. Li, R. Krishna, D. Xu, A. Byun, W. Shen, J. Braatz, D. Cai, J. Gwak, D.A. Huang, A. Kondrich, F. Y. Lin, D. Mrowca, B. Pan, N. Rai, L. P. Tchapmi, C. Waites, R. Wang, Y. Wen, K. Yang, B. Yi, C. Yuan, K. Zakka, and Y. Zhang. CS231n Convolutional Neural Networks for Visual Recognition. Available online: http://cs231n.stanford.edu/, Jan. 2015. [Online]. Q. Lou and L. Jiang. SHE: A fast and accurate privacy-preserving deep neural network via leveled TFHE and logarithmic data representation. CoRR, abs/1906.00148, 2019. L. Lu. Dying relu and initialization: Theory and numerical examples. Communications in Computational Physics, 28(5):1671–1706, June 2020. V. Lyubashevsky, C. Peikert, and O. Regev. On ideal lattices and learning with errors over rings. Cryptology ePrint Archive, Paper 2012/230, 2012. https://eprint.iacr.org/2012/230. P. Paillier. Public-key cryptosystems based on composite degree residuosity classes. In Advances in Cryptology —EUROCRYPT ’99, pages 223–238. Springer, 1999. O. Regev. On lattices, learning with errors, random linear codes, and cryptography. Journal of the ACM, 56(6):34:1–34:40, 2009. Earlier version in STOC 2005. A. S. Ronald L. Rivest and L. M. Adleman. A method for obtaining digital signatures and public-key cryptosystems. Communications of the ACM, 21(2):120–126, 1978. A. G. Schwing and R. Urtasun. Fully connected deep structured networks. CoRR, abs/1503.02351, 2015. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. CoRR, abs/1409.1556, 2014. D. Stehlé, R. Steinfeld, K. Tanaka, and K. Xagawa. Efficient public key encryption based on ideal lattices. Cryptology ePrint Archive, Paper 2009/285, 2009. https://eprint.iacr.org/2009/285. A. Stoian, J. Frery, R. Bredehoft, L. Montero, C. Kherfallah, and B. Chevallier-Mames. Deep neural networks for encrypted inference with tfhe. 2023. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93371 | - |
| dc.description.abstract | 全同態加密(FullyHomomorphicEncryption) 是能夠進行密文運算的加密方法。全同態加密能夠保護線上服務的使用者隱私，讓使用者能夠放心地將資訊上傳至雲端進行各式應用像是匿名投票、去中心化身份與機器學習。其中機器學習的應用在由於硬體上的限制，因此在推論時需要大量的時間，如何降低推論所需要的時間，在現在是一大挑戰。
先前研究使用了全同態加密中TFHE(FastFully Homomorphic Encryption Scheme Over the Torus) 的演算法實現VGG9機器學習模型，並且對於CIFAR10進行分類，本研究主要是針對車載系統上的影像使用全同態加密進行分類，使用不同的機器學習模型來判斷並分析準確度以及推論所需的時間，改善VGG9模型，在維持一定的準確度情況下，大幅節省推論所需的時間，由於現階段對於全同態加密還沒有特殊應用積體電路，因此僅能夠實現較小的模型，但是在未來有特殊應用積體電路出現後，可以參照本篇論文提出的各種優化方法來優化更大的神經網路模型。 | zh_TW |
| dc.description.abstract | Fully Homomorphic Encryption (FHE) is an encryption method that allows for operations on encrypted data. It protects the privacy of users of online services, enabling the secure upload of information to the cloud for various applications, such as anonymous voting, decentralized identity, and machine learning. One of the challenges, particularly in machine learning applications, is the significant amount of time required for inference due to hardware limitations.
Previous research implemented a VGG9 machine learning model using the TFHE (Fast Fully Homomorphic Encryption Scheme Over the Torus) algorithm to classify CIFAR10. This study focuses on using fully homomorphic encryption to classify images in smart vehicles. Different machine learning models are used to evaluate and analyze accuracy and inference time, aiming to improve upon the VGG9 model by significantly reducing inference time while maintaining a certain level of accuracy. Due to the current lack of specialized application-specific integrated circuits (ASICs) for fully holomorphic encryption, only smaller models can be realized. However, when such ASICs become available in the future, the various optimization methods proposed in this paper can be applied to larger neural network models. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-07-30T16:11:03Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-07-30T16:11:03Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
摘要 ii Abstract iii Contents v List of Figures viii List of Tables x Chapter1 Introduction 1 Chapter2 Background 5 2.1 Fully Homomorphic Encryption . . . . . . . . . . . . . . . . . . . . 5 2.1.1 Gentry’s Method . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.1.2 BGV & BFV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 2.1.3 GSW. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.4 CKKS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.1.5 TFHE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 TFHE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.1 LWE & RLWE . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 2.2.2 Addition & Scalar Multiplication . . . . . . . . . . . . . . . . . . . 10 2.2.3 Programmable Bootstrapping . . . . . . . . . . . . . . . . . . . . . 12 2.2.4 Machine Learning with TFHE . . . . . . . . . . . . . . . . . . . . 16 2.3 Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3.1 Deep Neural Network Architecture . . . . . . . . . . . . . . . . . . 18 2.3.2 Fully Connected Neural Networks . . . . . . . . . . . . . . . . . . 21 2.3.3 Convolutional Neural Networks . . . . . . . . . . . . . . . . . . . 22 2.3.4 Architecture of VGG . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.4 Quantization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.4.1 Post Training Quantization . . . . . . . . . . . . . . . . . . . . . . 29 2.4.2 Quantization Aware Training . . . . . . . . . . . . . . . . . . . . . 30 2.5 Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1 Unstructured Pruning . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.5.1.1 High Flexibility . . . . . . . . . . . . . . . . . . . . . 32 2.5.1.2 Fine-Grained Control . . . . . . . . . . . . . . . . . . 32 2.5.1.3 High Compression Rate . . . . . . . . . . . . . . . . . 32 2.5.1.4 Implementation Complexity. . . . . . . . . . . . . . . 32 2.5.2 Structured Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.5.2.1 Simpler Structure . . . . . . . . . . . . . . . . . . . . 33 2.5.2.2 Computational Efficiency . . . . . . . . . . . . . . . . 33 2.5.2.3 Ease of Implementation . . . . . . . . . . . . . . . . . 33 2.5.2.4 Improved Generalization . . . . . . . . . . . . . . . . 33 Chapter3 Related Works 34 Chapter4 Methodology 38 4.1 Fully Homomorphic Encryption-Based Privacy-Preserving Machine Learning Architecture for Smart Vehicles . . . . . . . . . . . . . . . 38 4.2 Machine Learning Model Architecture. . . . . . . . . . . . . . . . . 39 4.3 Quantization and Activation Functions . . . . . . . . . . . . . . . . 43 4.4 Pooling Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5 Input Preprocessing. . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.6 Structured Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Chapter5 Evaluation 46 5.1 Experimental Setup. . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.2 Benchmark . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 5.3 Evaluation Metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 5.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 Chapter6 Conclusion 56 References 58 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 全同態加密 | zh_TW |
| dc.subject | 隱私保護 | zh_TW |
| dc.subject | 量化 | zh_TW |
| dc.subject | 資料安全 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | machine learning | en |
| dc.subject | quantization | en |
| dc.subject | FHE | en |
| dc.subject | privacy protection | en |
| dc.subject | information security | en |
| dc.title | 基於全同態加密車載系統之兒童遺留偵測 | zh_TW |
| dc.title | Fully Homomorphic Encryption-Based Children Detection in Smart Vehicles | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 游家牧;陳英一;雷欽隆;顏嗣鈞 | zh_TW |
| dc.contributor.oralexamcommittee | Chia-Mu Yu;Ying-i Chen;Chin-Laung Lei;Hsu-Chun Yen | en |
| dc.subject.keyword | 全同態加密,機器學習,資料安全,量化,隱私保護, | zh_TW |
| dc.subject.keyword | FHE,machine learning,information security,quantization,privacy protection, | en |
| dc.relation.page | 61 | - |
| dc.identifier.doi | 10.6342/NTU202400842 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-07-30 | - |
| dc.contributor.author-college | 重點科技研究學院 | - |
| dc.contributor.author-dept | 積體電路設計與自動化學位學程 | - |
| dc.date.embargo-lift | 2029-07-25 | - |
| 顯示於系所單位： | 積體電路設計與自動化學位學程 | |
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