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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 吳家麟 | zh_TW |
dc.contributor.advisor | Ja-Ling Wu | en |
dc.contributor.author | 胡宮瑋 | zh_TW |
dc.contributor.author | Kung-Wei Hu | en |
dc.date.accessioned | 2024-08-15T17:02:29Z | - |
dc.date.available | 2024-08-16 | - |
dc.date.copyright | 2024-08-15 | - |
dc.date.issued | 2024 | - |
dc.date.submitted | 2024-08-01 | - |
dc.identifier.citation | Zvika Brakerski, Craig Gentry, and Vinod Vaikuntanathan. (leveled) fully homomorphic encryption without bootstrapping. ACM Transactions on Computation Theory (TOCT), 6(3):1–36, 2014.
Ilaria Chillotti, Nicolas Gama, Mariya Georgieva, and Malika Izabachène. Tfhe: fast fully homomorphic encryption over the torus. Journal of Cryptology, 33(1):34–91, 2020. Jung Hee Cheon, Andrey Kim, Miran Kim, and Yongsoo 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, pages 409–437. Springer, 2017. Michael Clear and Ciaran McGoldrick. Multi-identity and multi-key leveled fhe from learning with errors. In Advances in Cryptology–CRYPTO 2015: 35th Annual Cryptology Conference, Santa Barbara, CA, USA, August 16-20, 2015, Proceedings, Part II 35, pages 630–656. Springer, 2015. Craig Gentry. A fully homomorphic encryption scheme. Stanford university, 2009. Craig Gentry and Shai Halevi. Compressible fhe with applications to pir. In Theory of Cryptography Conference, pages 438–464. Springer, 2019. Craig Gentry, Amit Sahai, and Brent Waters. Homomorphic encryption from learning with errors: Conceptually-simpler, asymptotically-faster, attribute-based. In Advances in Cryptology–CRYPTO 2013: 33rd Annual Cryptology Conference, Santa Barbara, CA, USA, August 18-22, 2013. Proceedings, Part I, pages 75–92. Springer, 2013. Pratyay Mukherjee and Daniel Wichs. Two round multiparty computation via multi-key fhe. In Advances in Cryptology–EUROCRYPT 2016: 35th Annual International Conference on the Theory and Applications of Cryptographic Techniques, Vienna, Austria, May 8-12, 2016, Proceedings, Part II 35, pages 735–763. Springer, 2016. Chris Peikert, Vinod Vaikuntanathan, and Brent Waters. A framework for efficient and composable oblivious transfer. In Annual international cryptology conference, pages 554–571. Springer, 2008. Ronald L Rivest, Len Adleman, Michael L Dertouzos, et al. On data banks and privacy homomorphisms. Foundations of secure computation, 4(11):169–180, 1978. Oded Regev. On lattices, learning with errors, random linear codes, and cryptography. In 37th annual ACM symposium on Theory of computing, pages 84–93, 2005. Tongchen Shen, Fuqun Wang, Kefei Chen, Zhonghua Shen, and Renjun Zhang. Compressible multikey and multi-identity fully homomorphic encryption. Security and Communication Networks, 2021:1–14, 2021. Guangsheng Tu, Wenchao Liu, Tanping Zhou, Xiaoyuan Yang, and Fan Zhang. Concise and efficient multi-identity fully homomorphic encryption scheme. IEEE Access, 2024. Minghao Yuan, Dongdong Wang, Feng Zhang, Shenqing Wang, Shan Ji, and Yongjun Ren. An examination of multi-key fully homomorphic encryption and its applications. Mathematics, 10(24):4678, 2022. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/94360 | - |
dc.description.abstract | 當前的GSW加密方案的用戶信息選擇範圍有限。我們提出一種基於中國餘數定理(CRT)的信息分解方法,以解決這一限制並擴大數據範圍。該方法通過啟用平行操作並在密碼學領域內更高效地管理多個同態操作,克服了現有方法的低效率。這一優勢延伸至未來的GSW多鍵情境應用。我們也研究了GSW加密方案中過大的密文問題。通過整合當前的GSW多鍵設計並適應多鍵情境的壓縮方法,我們增強了其在實際密碼系統中的適用性。具體來說,我們使用CRT方法在多鍵情境中進行同態加法操作,並改進多鍵壓縮方法。最後,我們分別在聯邦學習和多方通信框架中展示了這些優化方法在多鍵操作中的性能,凸顯了我們方法在實際密碼學應用中的實用潛力。 | zh_TW |
dc.description.abstract | The current GSW encryption scheme has a limited range of user message options. We propose a message decomposition method based on the Chinese Remainder Theorem (CRT) to address this limitation and expand the data scope. This approach overcomes the inefficiencies of existing methods by enabling parallel operations and managing multiple homomorphic operations more efficiently within the cryptographic domain. This advantage extends to future applications in GSW multi-key scenarios. We also studied the issue of excessively large ciphertexts in the GSW encryption scheme. By integrating the current GSW multi-key design and adapting the compression method for multi-key scenarios, we enhance its applicability in real-world cryptosystems. Specifically, we use the CRT method to perform homomorphic addition operations in multi-key scenarios and improve the multi-key compression method. Finally, we demonstrate the performance of these optimization methods in multi-key operations using federated learning and multi-party communication frameworks, respectively, highlighting the practical potential of our methods in real-world cryptographic applications. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-15T17:02:29Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2024-08-15T17:02:29Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i
Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xiii List of Tables xvii Chapter 1 Introduction 1 Chapter 2 Related Work 3 2.1 Fully Homomorphic Encryption (FHE) 3 2.2 Ciphertext Compression 4 2.3 Organization 4 Chapter 3 Background 7 3.1 Learning With Error (LWE) 7 3.2 Gadget Matrices 8 3.3 CRT 9 3.4 Regev and PVW Encryption 10 Chapter 4 The Single-Key GSW Scheme 13 4.1 Initial Process 14 4.1.1 Key Generation 14 4.1.2 Encryption 15 4.1.3 Decryption 15 4.1.4 Homomorphic Operations 16 4.2 GSW-like Encryption Scheme 18 4.2.1 Key Generation 19 4.2.2 Encryption 19 4.2.3 Decryption 21 4.2.4 The Decryption Output 21 4.2.5 Homomorphic Operation 24 Chapter 5 The Multi-Key GSW Encryption Scheme 25 5.1 Key Generation 25 5.2 Encryption 27 5.3 Decryption 27 5.3.1 Correct key 28 5.3.2 The Effects of Using the Wrong Key 28 5.4 Details of the Expansion Processes 30 5.4.1 Part 1 30 5.4.2 Our improvement 34 5.4.3 Part 2 36 5.5 Comparison and Experiment 41 5.6 Distributed Decryption 48 5.7 Conclusion 51 Chapter 6 The CRT-based Decomposition and Homomorphic Additions 53 6.1 Base Decomposition (Figure 6.2, the middle block) 54 6.2 CRT Decomposition (Figure 6.2, the bottom block) 56 6.3 Homomorphic Addition after Decomposition 57 6.3.1 Base Decomposition 58 6.3.2 CRT Decomposition 59 6.4 Homomorphic Addition in Multikey 61 6.5 Comparative Analyses and Experimental Results 64 6.6 Federated Learning 67 Chapter 7 Ciphertext Compression 71 7.1 The Single-key PVW-like Scheme 72 7.1.1 Ideas and Concepts 73 7.1.2 Key Generation and Encryption 73 7.1.3 Compression 74 7.1.4 PVW-like Scheme's Decryption and Its Output 77 7.2 The Multikey PVW-like Scheme 79 7.2.1 Key Generation, Encryption and Expansion 79 7.2.2 The Compression Problems 80 7.3 The Proposed Multikey Ciphertext Compression 82 7.4 The Communications in the Multiparty Scheme 83 7.5 Experiment 85 Chapter 8 Future Works and Conclusions 87 References 89 Appendix A — Matrix Secret Key 93 A.1 Learning With Error (LWE) 93 A.2 Single Key 93 A.3 Multikey 94 A.3.1 Expansion (Part 1) 94 A.3.2 Expansion (our improvement) 95 A.3.3 Expansion (Part 2) 95 A.3.4 Comparison and Experiment 98 A.4 All Decomposition Process 100 A.5 Ciphertext Compression 101 A.5.1 PVW-like 102 A.5.2 Nearly Square Gadget Matrix 104 Appendix B — Proofs of Formulas 107 B.1 Pseudo Ciphertext Decryption (Part 1) 107 B.2 Pseudo Ciphertext Decryption (Part 2) 108 B.3 Accumulated Error for Same Key and Different Key Scenarios 109 | - |
dc.language.iso | en | - |
dc.title | GSW加密方案中基於中國剩餘定理的訊息資料範圍擴展和增強型多密鑰密文大小縮減方法 | zh_TW |
dc.title | CRT-Based Expansion of Message Data Range and an Enhanced Multi-Key Ciphertext Size Reduction Method for the GSW Encryption Scheme | en |
dc.type | Thesis | - |
dc.date.schoolyear | 112-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 許永真;陳文進;胡敏君;陳駿丞 | zh_TW |
dc.contributor.oralexamcommittee | Yung-Jen Hsu;Wen-Chin Chen;Min-Chun Hu;Jun-Cheng Chen | en |
dc.subject.keyword | GSW方案,中國剩餘定理,密文壓縮,多密鑰,聯邦學習, | zh_TW |
dc.subject.keyword | GSW scheme,CRT,Ciphertext compression,Multikey,Federated learning, | en |
dc.relation.page | 110 | - |
dc.identifier.doi | 10.6342/NTU202402654 | - |
dc.rights.note | 同意授權(全球公開) | - |
dc.date.accepted | 2024-08-03 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 資訊工程學系 | - |
顯示於系所單位: | 資訊工程學系 |
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