A Batch Verified Decentralized-AI Against Poisoning Attack In 6G Industrial CPS Environments

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：88

、訪客IP：3.146.255.135

姓名

詹益函(Yi-Han Chan) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

(A Batch Verified Decentralized-AI Against Poisoning Attack In 6G Industrial CPS Environments)

相關論文

★ DeFi 去中心化金融發展現況及風險分析- 以2017~2022 年 6 月為研究區間	★ 基於區塊鏈防止雙重投票的匿名投票系統
★ NFT-based 車輛與零件履歷驗證平台	★ 基於區塊鏈與代理重新加密之隱私保護威脅情資分享平台
★ 元宇宙與 NFT 應用於旅遊購物虛實整合架構規劃之研究	★ Decentralizing DNS: Leveraging Web3 and Blockchain Smart Contracts for Enhanced Security
★ Innovations in 6G: Decentralized Network Slice Handover with Proxy Re-Encryption	★ Blockchain-based Federated learning with Data privacy protection
★ 具公正性抽獎機制與隱私防護之問卷平台	★ 建立安全可靠的推薦信平台: 基於分散式系統的創新方法
★ 去中心化電子書交易平台之區塊鏈框架設計與可行性分析	★ A Decentralized Group-oriented Information Sharing System with Searchable Encryption in Supply Chain Environment
★ A Blockchain-based Work Performance Authenticity Platform with User Incentive Mechanism	★ 基於區塊鏈與存取控制之多媒體分享平台

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2028-7-1以後開放)

摘要(中)

隨著Cyber-Physical Systems (CPS) 設備的快速增長，也因此而產生了大量的數據，使得數據驅動的應用得以蓬勃發展，如智慧型系統的開發與工業4.0的實現。也因為6G網路能夠提供更快的傳輸速度和更強的連接性，進而促進了工業環境下CPS設備的部署和基於人工智慧之服務的普及性。然而傳統的分散式機器學習架構如聯邦學習，面臨了重大的安全性威脅及挑戰，像是隱私洩露和單點故障問題。此外，合作式的機器學習系統也需要強大的防禦措施來抵禦投毒攻擊之威脅。為了克服上述問題，此篇論文為工業環境下的CPS提出了一種分散式機器學習架構，利用區塊鏈技術和批次驗證，有效解決單點故障和未經授權的模型更新上傳。也提出基於Multi-KRUM演算法的模型選擇方案和利用區塊鏈實現的certificate revocation list進一步對抗了投毒攻擊。另外，此架構中的本地差分隱私機制還保證了使用者隱私，避免遭受推理攻擊。最後本篇論文中還增加了獎勵機制，使用代幣獎勵提供額外算力的參與者，從而促進合作關係以提高模型的整體準確度。在最後的實驗結果也能夠看出，本論文所提出的全面性框架增強了ICPS環境下分散式機器學習的安全性、可靠性和隱私性。

摘要(英)

Rapid growth in Cyber-Physical Systems (CPS) devices has resulted in massive data generation, enabling the development of data-driven applications such as smart system development and Industry 4.0 realization. With the 6G network promises faster transmission speeds and stronger connectivity, fostering wider adoption of Industrial CPS devices and effective AI-based services. However, conventional distributed machine learning approaches like federated learning pose significant security challenges, such as privacy breaches and vulnerability to single points of failure. Additionally, the threat of poisoning attacks in collaborative learning systems necessitates robust defenses. To overcome these, we propose a decentralized machine learning approach for Industrial CPS that harnesses blockchain technology and batch verification, efficiently addressing single point failures and unauthorized submissions of model updates. A model selection scheme based on the Multi-KRUM algorithm and a blockchain-implemented certificate revocation list further counteract poisoning attacks. The application of local differential privacy mechanism secures client privacy against inference attacks. Finally, the use of incentive tokens serves as a motivator for clients to contribute their training results, thus promoting collaboration and improving the overall quality of the artificial intelligence model. The experimental results presented in Section Six provide compelling evidence that our comprehensive framework enhances security, reliability, and privacy in distributed machine learning within the ICPS environment.

關鍵字(中)

★ 分散式人工智慧
★ 區塊鏈
★ 批次驗證
★ 6G
★ CPS
★ 投毒攻擊
★ 隱私保護

關鍵字(英)

★ Decentralized-AI
★ Blockchain
★ Batch Verification
★ 6G
★ CPS
★ Poisoning Attack
★ Privacy-Preserving

論文目次

摘要 i
Abstract ii
Table of Contents iii
List of Figures v
List of Tables vi
1 INTRODUCTION 1
1.1 Background 1
1.2 Motivation 2
1.3 Purpose 3
2 RELATED WORKS 5
2.1 Blockchain 5
2.2 IPFS 6
2.3 Distributed Machine Learning 7
2.3.1 Data Parallel 7
2.3.2 Model Parallel 7
2.3.3 Federated Learning 7
2.4 Data-driven Application for CPS 8
3 PRELIMINARIES 10
3.1 Notations and Definitions 10
3.2 Differential Privacy 10
3.2.1 Traditional (Central) Differential Privacy (CDP): 11
3.2.2 Local Differential Privacy (LDP): 11
3.3 Batch Verification 11
3.3.1 Bilinear Maps 12
3.4 Multi-Krum 12
3.4.1 KRUM Algorithm 13
3.4.2 Multi-KRUM Algorithm 13
4 PROBLEM STATEMENT 15
4.1 Design Goals 15
4.2 Threat Models 15
5 PROPOSED FRAMEWORK 17
5.1 System Model 17
5.1.1 Clients (Participants) 18
5.1.2 Model Auditor & Aggregator 19
5.1.3 Blockchain Smart Contract 19
5.2 System Overview 21
5.3 System Component 22
5.3.1 Generation of Local Model 22
5.3.2 Batch Verification 24
5.3.3 Model Examination 25
5.3.4 Verified Model Aggregation 27
6 SECURITY ANALYSIS AND EXPERIMENT 29
6.1 Comparative Summary 29
6.2 Security Analysis 30
6.2.1 Upload Authenticity 30
6.2.2 Decentralization and Transparency 31
6.2.3 Fair incentive mechanism 31
6.3 Experiment 32
6.3.1 Experimental Settings 32
6.3.2 Experiments Methods 33
6.3.3 Experiments Results 33
6.3.3.1 Influence of Privacy Budget 33
6.3.3.2 The Accuracy of Different Aggregation Algorithms 34
6.3.3.3 Impact of Poisoning Attack: 36
6.3.3.4 Batch Verification Efficiency: 37
7 CONCLUSION 39
REFERENCE 40

參考文獻

[1] B. A. Salau, A. Rawal, and D. B. Rawat, “Recent Advances in Artificial Intelligence for Wireless Internet of Things and Cyber–Physical Systems: A Comprehensive Survey,” IEEE Internet of Things Journal, vol. 9, no. 15, pp. 12916–12930, Aug. 2022, doi: 10.1109/JIOT.2022.3170449.
[2] J. Wan, J. Li, M. Imran, D. Li, and Fazal-e-Amin, “A Blockchain-Based Solution for Enhancing Security and Privacy in Smart Factory,” IEEE Transactions on Industrial Informatics, vol. 15, no. 6, pp. 3652–3660, Jun. 2019, doi: 10.1109/TII.2019.2894573.
[3] P. Ajay, B. Nagaraj, B. M. Pillai, J. Suthakorn, and M. Bradha, “Intelligent ecofriendly transport management system based on IoT in urban areas,” Environ Dev Sustain, Jan. 2022, doi: 10.1007/s10668-021-02010-x.
[4] Y. Yang, H. Wang, R. Jiang, X. Guo, J. Cheng, and Y. Chen, “A Review of IoT-Enabled Mobile Healthcare: Technologies, Challenges, and Future Trends,” IEEE Internet of Things Journal, vol. 9, no. 12, pp. 9478–9502, Jun. 2022, doi: 10.1109/JIOT.2022.3144400.
[5] U. M. Malik, M. A. Javed, S. Zeadally, and S. ul Islam, “Energy-Efficient Fog Computing for 6G-Enabled Massive IoT: Recent Trends and Future Opportunities,” IEEE Internet of Things Journal, vol. 9, no. 16, pp. 14572–14594, Aug. 2022, doi: 10.1109/JIOT.2021.3068056.
[6] W. Saad, M. Bennis, and M. Chen, “A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems,” IEEE Network, vol. 34, no. 3, pp. 134–142, May 2020, doi: 10.1109/MNET.001.1900287.
[7] M. Giordani, M. Polese, M. Mezzavilla, S. Rangan, and M. Zorzi, “Toward 6G Networks: Use Cases and Technologies,” IEEE Communications Magazine, vol. 58, no. 3, pp. 55–61, Mar. 2020, doi: 10.1109/MCOM.001.1900411.
[8] F. Tang, Y. Kawamoto, N. Kato, and J. Liu, “Future Intelligent and Secure Vehicular Network Toward 6G: Machine-Learning Approaches,” Proceedings of the IEEE, vol. 108, no. 2, pp. 292–307, Feb. 2020, doi: 10.1109/JPROC.2019.2954595.
[9] H. Wang et al., “Attack of the Tails: Yes, You Really Can Backdoor Federated Learning,” in Advances in Neural Information Processing Systems, Curran Associates, Inc., 2020, pp. 16070–16084. Accessed: Jun. 19, 2023. [Online]. Available: https://proceedings.neurips.cc/paper/2020/hash/b8ffa41d4e492f0fad2f13e29e1762eb-Abstract.html
[10] J. Gao, B. Zhang, X. Guo, T. Baker, M. Li, and Z. Liu, “Secure Partial Aggregation: Making Federated Learning More Robust for Industry 4.0 Applications,” IEEE Trans. Ind. Inf., vol. 18, no. 9, pp. 6340–6348, Sep. 2022, doi: 10.1109/TII.2022.3145837.
[11] H. Li, X. Sun, and Z. Zheng, “Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework,” Advances in Neural Information Processing Systems, vol. 35, pp. 35007–35020, Dec. 2022.
[12] S. Savazzi, M. Nicoli, and V. Rampa, “Federated Learning With Cooperating Devices: A Consensus Approach for Massive IoT Networks,” IEEE Internet Things J., vol. 7, no. 5, pp. 4641–4654, May 2020, doi: 10.1109/JIOT.2020.2964162.
[13] C. Ma et al., “When Federated Learning Meets Blockchain: A New Distributed Learning Paradigm,” IEEE Comput. Intell. Mag., vol. 17, no. 3, pp. 26–33, Aug. 2022, doi: 10.1109/MCI.2022.3180932.
[14] X. Bao, C. Su, Y. Xiong, W. Huang, and Y. Hu, “FLChain: A Blockchain for Auditable Federated Learning with Trust and Incentive,” 2019 5th International Conference on Big Data Computing and Communications (BIGCOM), pp. 151–159, Aug. 2019, doi: 10.1109/BIGCOM.2019.00030.
[15] Y. Zhang, Y. Liang, B. Jia, P. Wang, and X. Zhang, “A blockchain‐enabled learning model based on distributed deep learning architecture,” Int J of Intelligent Sys, vol. 37, no. 9, pp. 6577–6604, Sep. 2022, doi: 10.1002/int.22907.
[16] Y. Khazbak, T. Tan, and G. Cao, “MLGuard: Mitigating Poisoning Attacks in Privacy Preserving Distributed Collaborative Learning,” 2020 29th International Conference on Computer Communications and Networks (ICCCN), pp. 1–9, Aug. 2020, doi: 10.1109/ICCCN49398.2020.9209670.
[17] M. Jagielski, A. Oprea, B. Biggio, C. Liu, C. Nita-Rotaru, and B. Li, “Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning,” 2018 IEEE Symposium on Security and Privacy (SP), pp. 19–35, May 2018, doi: 10.1109/SP.2018.00057.
[18] C. Fung, C. J. M. Yoon, and I. Beschastnikh, “Mitigating Sybils in Federated Learning Poisoning,” ArXiv, Aug. 2018, Accessed: Jun. 19, 2023. [Online]. Available: https://www.semanticscholar.org/paper/Mitigating-Sybils-in-Federated-Learning-Poisoning-Fung-Yoon/333420606f059a7d5574a6fb9e35591346d3f957
[19] X. Qiao, Y. Huang, S. Dustdar, and J. Chen, “6G Vision: An AI-Driven Decentralized Network and Service Architecture,” IEEE Internet Comput., vol. 24, no. 4, pp. 33–40, Jul. 2020, doi: 10.1109/MIC.2020.2987738.
[20] Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,” 2017 IEEE International Congress on Big Data (BigData Congress), pp. 557–564, Jun. 2017, doi: 10.1109/BigDataCongress.2017.85.
[21] V. Buterin, “A next-generation smart contract and decentralized application platform,” white paper, vol. 3, no. 37, pp. 2–1, 2014.
[22] M. Swan, “Blockchain: Blueprint for a New Economy,” 2015.
[23] F. Victor and B. K. Lüders, “Measuring Ethereum-Based ERC20 Token Networks,” in Financial Cryptography and Data Security, I. Goldberg and T. Moore, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, pp. 113–129. doi: 10.1007/978-3-030-32101-7_8.
[24] J. Benet, “Ipfs-content addressed, versioned, p2p file system,” arXiv preprint arXiv:1407.3561, 2014.
[25] A. Galakatos, A. Crotty, and T. Kraska, “Distributed Machine Learning,” in Encyclopedia of Database Systems, L. Liu and M. T. Özsu, Eds., New York, NY: Springer, 2018, pp. 1196–1201. doi: 10.1007/978-1-4614-8265-9_80647.
[26] J. Verbraeken, M. Wolting, J. Katzy, J. Kloppenburg, T. Verbelen, and J. S. Rellermeyer, “A Survey on Distributed Machine Learning,” ACM Comput. Surv., vol. 53, no. 2, p. 30:1-30:33, Mar. 2020, doi: 10.1145/3377454.
[27] P. Richtárik and M. Takáč, “Distributed coordinate descent method for learning with big data,” J. Mach. Learn. Res., vol. 17, no. 1, pp. 2657–2681, Jan. 2016.
[28] J. B. Predd, S. B. Kulkarni, and H. V. Poor, “Distributed learning in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 23, no. 4, pp. 56–69, Jul. 2006, doi: 10.1109/MSP.2006.1657817.
[29] S. Fan et al., “DAPPLE: a pipelined data parallel approach for training large models,” in Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, in PPoPP ’21. New York, NY, USA: Association for Computing Machinery, Feb. 2021, pp. 431–445. doi: 10.1145/3437801.3441593.
[30] J. Du et al., “Model Parallelism Optimization for Distributed Inference via Decoupled CNN Structure,” IEEE Trans. Parallel Distrib. Syst., pp. 1–1, 2020, doi: 10.1109/TPDS.2020.3041474.
[31] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated Machine Learning: Concept and Applications,” ACM Trans. Intell. Syst. Technol., vol. 10, no. 2, p. 12:1-12:19, Jan. 2019, doi: 10.1145/3298981.
[32] J. Konecný, H. B. McMahan, F. X. Yu, P. Richtárik, A. Suresh, and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency,” ArXiv, Oct. 2016, Accessed: Mar. 16, 2023. [Online]. Available: https://www.semanticscholar.org/paper/Federated-Learning%3A-Strategies-for-Improving-Konecn%C3%BD-McMahan/7fcb90f68529cbfab49f471b54719ded7528d0ef
[33] R. Shokri and V. Shmatikov, “Privacy-Preserving Deep Learning,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, in CCS ’15. New York, NY, USA: Association for Computing Machinery, Oct. 2015, pp. 1310–1321. doi: 10.1145/2810103.2813687.
[34] J. Yuan and S. Yu, “Privacy Preserving Back-Propagation Neural Network Learning Made Practical with Cloud Computing,” IEEE Transactions on Parallel and Distributed Systems, vol. 25, no. 1, pp. 212–221, Jan. 2014, doi: 10.1109/TPDS.2013.18.
[35] P. Kairouz et al., “Advances and Open Problems in Federated Learning,” FNT in Machine Learning, vol. 14, no. 1–2, pp. 1–210, 2021, doi: 10.1561/2200000083.
[36] I. Hegedűs, G. Danner, and M. Jelasity, “Gossip Learning as a Decentralized Alternative to Federated Learning,” in Distributed Applications and Interoperable Systems, J. Pereira and L. Ricci, Eds., in Lecture Notes in Computer Science. Cham: Springer International Publishing, 2019, pp. 74–90. doi: 10.1007/978-3-030-22496-7_5.
[37] E. Bagdasaryan, A. Veit, Y. Hua, D. Estrin, and V. Shmatikov, “How To Backdoor Federated Learning,” in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, PMLR, Jun. 2020, pp. 2938–2948. Accessed: Mar. 21, 2023. [Online]. Available: https://proceedings.mlr.press/v108/bagdasaryan20a.html
[38] L. Melis, C. Song, E. De Cristofaro, and V. Shmatikov, “Exploiting Unintended Feature Leakage in Collaborative Learning,” 2019 IEEE Symposium on Security and Privacy (SP), pp. 691–706, May 2019, doi: 10.1109/SP.2019.00029.
[39] H. B. McMahan, D. Ramage, K. Talwar, and L. Zhang, “Learning Differentially Private Recurrent Language Models,” presented at the International Conference on Learning Representations, Oct. 2017. Accessed: Mar. 20, 2023. [Online]. Available: https://www.semanticscholar.org/paper/Learning-Differentially-Private-Recurrent-Language-McMahan-Ramage/ed46493d568030b42f0154d9e5bf39bbd07962b3
[40] B. Et-taibi, M. R. Abid, I. Boumhidi, and D. Benhaddou, “Smart Agriculture as a Cyber Physical System: A Real-World Deployment,” in 2020 Fourth International Conference On Intelligent Computing in Data Sciences (ICDS), Oct. 2020, pp. 1–7. doi: 10.1109/ICDS50568.2020.9268734.
[41] O. Friha, M. A. Ferrag, L. Shu, L. Maglaras, and X. Wang, “Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies,” IEEE/CAA Journal of Automatica Sinica, vol. 8, no. 4, pp. 718–752, Apr. 2021, doi: 10.1109/JAS.2021.1003925.
[42] V. Udutalapally, S. P. Mohanty, V. Pallagani, and V. Khandelwal, “sCrop: A Novel Device for Sustainable Automatic Disease Prediction, Crop Selection, and Irrigation in Internet-of-Agro-Things for Smart Agriculture,” IEEE Sensors Journal, vol. 21, no. 16, pp. 17525–17538, Aug. 2021, doi: 10.1109/JSEN.2020.3032438.
[43] G. White, A. Zink, L. Codecá, and S. Clarke, “A digital twin smart city for citizen feedback,” Cities, vol. 110, p. 103064, Mar. 2021, doi: 10.1016/j.cities.2020.103064.
[44] A. Kirimtat, O. Krejcar, A. Kertesz, and M. F. Tasgetiren, “Future Trends and Current State of Smart City Concepts: A Survey,” IEEE Access, vol. 8, pp. 86448–86467, 2020, doi: 10.1109/ACCESS.2020.2992441.
[45] P. O’Donovan, K. Leahy, K. Bruton, and D. T. J. O’Sullivan, “An industrial big data pipeline for data-driven analytics maintenance applications in large-scale smart manufacturing facilities,” Journal of Big Data, vol. 2, no. 1, p. 25, Nov. 2015, doi: 10.1186/s40537-015-0034-z.
[46] W. Wang, Y. Zhang, J. Gu, and J. Wang, “A Proactive Manufacturing Resources Assignment Method Based on Production Performance Prediction for the Smart Factory,” IEEE Transactions on Industrial Informatics, vol. 18, no. 1, pp. 46–55, Jan. 2022, doi: 10.1109/TII.2021.3073404.
[47] A. Napoleone, E. Negri, M. Macchi, and A. Pozzetti, “How the technologies underlying cyber-physical systems support the reconfigurability capability in manufacturing: a literature review,” International Journal of Production Research, vol. 61, no. 9, pp. 3122–3144, May 2023, doi: 10.1080/00207543.2022.2074323.
[48] M. A. Ferrag and L. Maglaras, “DeepCoin: A Novel Deep Learning and Blockchain-Based Energy Exchange Framework for Smart Grids,” IEEE Transactions on Engineering Management, vol. 67, no. 4, pp. 1285–1297, Jan. 2020, doi: 10.1109/TEM.2019.2922936.
[49] A. Yazdinejad, A. Dehghantanha, R. M. Parizi, M. Hammoudeh, H. Karimipour, and G. Srivastava, “Block Hunter: Federated Learning for Cyber Threat Hunting in Blockchain-Based IIoT Networks,” IEEE Transactions on Industrial Informatics, vol. 18, no. 11, pp. 8356–8366, Jan. 2022, doi: 10.1109/TII.2022.3168011.
[50] C. Dwork, “Differential Privacy,” in Automata, Languages and Programming, M. Bugliesi, B. Preneel, V. Sassone, and I. Wegener, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2006, pp. 1–12. doi: 10.1007/11787006_1.
[51] Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato, and A. A. A. El-Latif, “A Secure Federated Learning Framework for 5G Networks,” IEEE Wireless Communications, vol. 27, no. 4, pp. 24–31, Aug. 2020, doi: 10.1109/MWC.01.1900525.
[52] C. Dwork, “Differential Privacy: A Survey of Results,” in Theory and Applications of Models of Computation, M. Agrawal, D. Du, Z. Duan, and A. Li, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2008, pp. 1–19. doi: 10.1007/978-3-540-79228-4_1.
[53] M. Abadi et al., “Deep Learning with Differential Privacy,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security, in CCS ’16. New York, NY, USA: Association for Computing Machinery, Oct. 2016, pp. 308–318. doi: 10.1145/2976749.2978318.
[54] M. Du, K. Wang, Z. Xia, and Y. Zhang, “Differential Privacy Preserving of Training Model in Wireless Big Data with Edge Computing,” IEEE Transactions on Big Data, vol. 6, no. 2, pp. 283–295, Jun. 2020, doi: 10.1109/TBDATA.2018.2829886.
[55] P. Kairouz, S. Oh, and P. Viswanath, “Extremal Mechanisms for Local Differential Privacy,” in Advances in Neural Information Processing Systems, Z. Ghahramani, M. Welling, C. Cortes, N. Lawrence, and K. Q. Weinberger, Eds., Curran Associates, Inc., 2014. [Online]. Available: https://proceedings.neurips.cc/paper_files/paper/2014/file/86df7dcfd896fcaf2674f757a2463eba-Paper.pdf
[56] G. Cormode, S. Jha, T. Kulkarni, N. Li, D. Srivastava, and T. Wang, “Privacy at Scale: Local Differential Privacy in Practice,” in Proceedings of the 2018 International Conference on Management of Data, in SIGMOD ’18. New York, NY, USA: Association for Computing Machinery, May 2018, pp. 1655–1658. doi: 10.1145/3183713.3197390.
[57] A. S. Kittur and A. R. Pais, “Batch verification of Digital Signatures: Approaches and challenges,” Journal of Information Security and Applications, vol. 37, pp. 15–27, Dec. 2017, doi: 10.1016/j.jisa.2017.09.005.
[58] A. L. Ferrara, M. Green, S. Hohenberger, and M. Ø. Pedersen, “Practical Short Signature Batch Verification,” in Topics in Cryptology – CT-RSA 2009, M. Fischlin, Ed., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2009, pp. 309–324. doi: 10.1007/978-3-642-00862-7_21.
[59] C. Zhang, R. Lu, X. Lin, P.-H. Ho, and X. Shen, “An Efficient Identity-Based Batch Verification Scheme for Vehicular Sensor Networks,” in IEEE INFOCOM 2008 - The 27th Conference on Computer Communications, Apr. 2008, pp. 246–250. doi: 10.1109/INFOCOM.2008.58.
[60] E. M. E. Mhamdi, R. Guerraoui, and S. Rouault, “The Hidden Vulnerability of Distributed Learning in Byzantium,” in Proceedings of the 35th International Conference on Machine Learning, PMLR, Jul. 2018, pp. 3521–3530. Accessed: Jun. 14, 2023. [Online]. Available: https://proceedings.mlr.press/v80/mhamdi18a.html
[61] L. Chen, H. Wang, Z. B. Charles, and D. Papailiopoulos, “DRACO: Byzantine-resilient Distributed Training via Redundant Gradients,” presented at the International Conference on Machine Learning, Mar. 2018. Accessed: Jun. 13, 2023. [Online]. Available: https://www.semanticscholar.org/paper/DRACO%3A-Byzantine-resilient-Distributed-Training-via-Chen-Wang/31f8806397907e197ca1d3676f598fd197087ad6
[62] “Practical Byzantine fault tolerance | Proceedings of the third symposium on Operating systems design and implementation.” https://dl.acm.org/doi/10.5555/296806.296824 (accessed Jun. 13, 2023).
[63] P. Blanchard, E. M. E. Mhamdi, R. Guerraoui, and J. Stainer, “Machine Learning with Adversaries: Byzantine Tolerant Gradient Descent,” presented at the NIPS, Dec. 2017. Accessed: Mar. 21, 2023. [Online]. Available: https://www.semanticscholar.org/paper/Machine-Learning-with-Adversaries%3A-Byzantine-Blanchard-Mhamdi/9583ac53a19cdf0db81fef6eb0b63e66adbe2324
[64] C. Dwork, F. McSherry, K. Nissim, and A. Smith, “Calibrating Noise to Sensitivity in Private Data Analysis,” in Theory of Cryptography, S. Halevi and T. Rabin, Eds., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2006, pp. 265–284. doi: 10.1007/11681878_14.
[65] D. Boneh, B. Lynn, and H. Shacham, “Short Signatures from the Weil Pairing,” in Advances in Cryptology — ASIACRYPT 2001, C. Boyd, Ed., in Lecture Notes in Computer Science. Berlin, Heidelberg: Springer, 2001, pp. 514–532. doi: 10.1007/3-540-45682-1_30.
[66] B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, “Communication-Efficient Learning of Deep Networks from Decentralized Data,” in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR, Apr. 2017, pp. 1273–1282. Accessed: Jun. 17, 2023. [Online]. Available: https://proceedings.mlr.press/v54/mcmahan17a.html
[67] X. Cao, Z. Zhang, J. Jia, and N. Z. Gong, “FLCert: Provably Secure Federated Learning Against Poisoning Attacks,” IEEE Transactions on Information Forensics and Security, vol. 17, pp. 3691–3705, 2022, doi: 10.1109/TIFS.2022.3212174.
[68] P. Zhao, Z. Cao, J. Jiang, and F. Gao, “Practical Private Aggregation in Federated Learning Against Inference Attack,” IEEE Internet of Things Journal, vol. 10, no. 1, pp. 318–329, Jan. 2023, doi: 10.1109/JIOT.2022.3201231.
[69] H. Liao et al., “Blockchain and Semi-Distributed Learning-Based Secure and Low-Latency Computation Offloading in Space-Air-Ground-Integrated Power IoT,” IEEE Journal of Selected Topics in Signal Processing, vol. 16, no. 3, pp. 381–394, Apr. 2022, doi: 10.1109/JSTSP.2021.3135751.
[70] Y. Qi, M. S. Hossain, J. Nie, and X. Li, “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” Future Generation Computer Systems, vol. 117, pp. 328–337, Apr. 2021, doi: 10.1016/j.future.2020.12.003.
[71] M. Xu, Z. Zou, Y. Cheng, Q. Hu, D. Yu, and X. Cheng, “SPDL: A Blockchain-Enabled Secure and Privacy-Preserving Decentralized Learning System,” IEEE Transactions on Computers, vol. 72, no. 2, pp. 548–558, Feb. 2023, doi: 10.1109/TC.2022.3169436.
[72] L. Deng, “The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web],” IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 141–142, Jan. 2012, doi: 10.1109/MSP.2012.2211477.
[73] A. Krizhevsky, “Learning Multiple Layers of Features from Tiny Images”.
[74] A. P. Kalapaaking, I. Khalil, M. S. Rahman, M. Atiquzzaman, X. Yi, and M. Almashor, “Blockchain-Based Federated Learning With Secure Aggregation in Trusted Execution Environment for Internet-of-Things,” IEEE Transactions on Industrial Informatics, vol. 19, no. 2, pp. 1703–1714, Feb. 2023, doi: 10.1109/TII.2022.3170348.
[75] Y. Zhao et al., “Privacy-Preserving Blockchain-Based Federated Learning for IoT Devices,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1817–1829, Feb. 2021, doi: 10.1109/JIOT.2020.3017377.
[76] P. Kumar, S. Kumari, V. Sharma, X. Li, A. K. Sangaiah, and S. H. Islam, “Secure CLS and CL-AS schemes designed for VANETs,” J Supercomput, vol. 75, no. 6, pp. 3076–3098, Jun. 2019, doi: 10.1007/s11227-018-2312-y.

指導教授

葉羅堯(Lo-Yao Yeh)

審核日期

2023-7-25

推文