| 研究生: |
陳子顥 Chen, Tzu-Hao |
|---|---|
| 論文名稱: |
基於CKKS加密方法實現雲端深度學習資料保護 Cloud Deep Learning Data Protection Based on CKKS Encryption Method |
| 指導教授: |
廖德祿
Liao, Teh-Lu |
| 學位類別: |
碩士 Master |
| 系所名稱: |
工學院 - 工程科學系 Department of Engineering Science |
| 論文出版年: | 2024 |
| 畢業學年度: | 112 |
| 語文別: | 中文 |
| 論文頁數: | 63 |
| 中文關鍵詞: | 全同態加密 、深度學習 、雲端運算 |
| 外文關鍵詞: | Fully Homomorphic Encryption, Deep Learning, Cloud Computing |
| 相關次數: | 點閱:40 下載:0 |
| 分享至: |
| 查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報 |
[1] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, Nov. 1998.
[2] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," 2012.
[3] S. Karen and Z. Andrew, "Very Deep Convolutional Networks for Large-Scale Image Recognition," CoRR, 2014. [Online]. Available: https://api.semanticscholar.org/CorpusID:14124313.
[4] 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, pp. 770-778.
[5] G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, "Densely connected convolutional networks," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, pp. 4700-4708.
[6] T. Elgamal, "A public key cryptosystem and a signature scheme based on discrete logarithms," IEEE Transactions on Information Theory, vol. 31, no. 4, pp. 469-472, 1985.
[7] P. Paillier, "Public-Key Cryptosystems Based on Composite Degree Residuosity Classes," in Advances in Cryptology — EUROCRYPT ’99, Berlin, Heidelberg, J. Stern, Ed., 1999// 1999: Springer Berlin Heidelberg, pp. 223-238.
[8] J. Fan and F. Vercauteren, "Somewhat practical fully homomorphic encryption," Cryptology ePrint Archive, 2012.
[9] Z. Brakerski, "Fully homomorphic encryption without modulus switching from classical GapSVP," in Annual cryptology conference, 2012, vol. 7417: Springer, Berlin, Heidelberg, pp. 868-886.
[10] C. Gentry, "Fully homomorphic encryption using ideal lattices," in Proceedings of the forty-first annual ACM symposium on Theory of computing, Bethesda, Maryland, USA., 2009, pp. 169-178.
[11] Z. Brakerski, C. Gentry, and V. Vaikuntanathan, "(Leveled) fully homomorphic encryption without bootstrapping," ACM Transactions on Computation Theory (TOCT), vol. 6, no. 3, pp. 1-36, 2014.
[12] J. H. Cheon, A. Kim, M. Kim, and Y. Song, "Homomorphic encryption for arithmetic of approximate numbers," in Advances in Cryptology–ASIACRYPT 2017: 23rd International Conference on the Theory and Applications of Cryptology and Information Security, Hong Kong, China, December 3-7, 2017, Proceedings, Part I 23, 2017: Springer, pp. 409-437.
[13] M. Li, L. Lai, N. Suda, V. Chandra, and D. Z. Pan, "Privynet: A flexible framework for privacy-preserving deep neural network training," arXiv preprint arXiv:1709.06161, 2017.
[14] 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. 13, no. 5, pp. 1333-1345, 2017.
[15] A. Falcetta and M. Roveri, "Privacy-preserving deep learning with homomorphic encryption: An introduction," IEEE Computational Intelligence Magazine, vol. 17, no. 3, pp. 14-25, 2022.
[16] 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, New York, NY, USA, 2016, no. 48: PMLR, pp. 201-210.
[17] "Microsoft SEAL (release 4.1)," 2023/1 2023. [Online]. Available: https://github.com/Microsoft/SEAL.
[18] L. Deng, "The mnist database of handwritten digit images for machine learning research," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 141-142, 2012 2012.
[19] R. L. Rivest, A. Shamir, and L. Adleman, "A method for obtaining digital signatures and public-key cryptosystems," Communications of the ACM, vol. 21, no. 2, pp. 120-126, 1978.
[20] W. Diffie and M. E. Hellman, "New directions in cryptography," in Democratizing Cryptography: The Work of Whitfield Diffie and Martin Hellman, 2022, pp. 365-390.
[21] N. Koblitz, "Elliptic curve cryptosystems," Mathematics of computation, vol. 48, no. 177, pp. 203-209, 1987.
[22] M. Ajtai, "Generating hard instances of lattice problems," in Proceedings of the twenty-eighth annual ACM symposium on Theory of computing, 1996, pp. 99-108.
[23] M. Ajtai and C. Dwork, "A public-key cryptosystem with worst-case/average-case equivalence," in Proceedings of the twenty-ninth annual ACM symposium on Theory of computing, 1997, pp. 284-293.
[24] O. Regev, "On lattices, learning with errors, random linear codes, and cryptography," Journal of the ACM (JACM), vol. 56, no. 6, pp. 1-40, 2009.
[25] M. R. Albrecht, R. Player, and S. Scott, "On the concrete hardness of learning with errors," Journal of Mathematical Cryptology, vol. 9, no. 3, pp. 169-203, 2015.
[26] V. Lyubashevsky, C. Peikert, and O. Regev, "On ideal lattices and learning with errors over rings," in Advances in Cryptology–EUROCRYPT 2010: 29th Annual International Conference on the Theory and Applications of Cryptographic Techniques, French Riviera, May 30–June 3, 2010. Proceedings 29, 2010: Springer, pp. 1-23.
[27] A. Martin et al., "Homomorphic Encryption Security Standard , institution= HomomorphicEncryption.org," November 2018.
[28] M. Albrecht et al., "Homomorphic Encryption Standard," in Protecting Privacy through Homomorphic Encryption, K. Lauter, W. Dai, and K. Laine Eds. Cham: Springer International Publishing, 2021, pp. 31-62.
[29] W. Cukierski, "Dogs vs. Cats," 2013 2013. [Online]. Available: https://kaggle.com/competitions/dogs-vs-cats.
[30] S. J. Pan and Q. Yang, "A survey on transfer learning," IEEE Transactions on knowledge and data engineering, vol. 22, no. 10, pp. 1345-1359, 2009.
[31] J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, "Imagenet: A large-scale hierarchical image database," presented at the 2009 IEEE conference on computer vision and pattern recognition, 2009.
[32] J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 779-788.
校內:2029-08-14公開