參考文獻 |
[1] P. Voigt and A. Von dem Bussche, “The eu general data protection regulation (gdpr),” A Practical Guide, 1st Ed., Cham: Springer International Publishing, vol. 10, no. 3152676, pp. 10–5555, 2017.
[2] J. Konecnˇ y, H. B. McMahan, F. X. Yu, P. Richt ́ arik, A. T. ́ Suresh, and D. Bacon, “Federated Learning: Strategies for Improving Communication Efficiency,” 2017. [Online]. Available: http://arxiv.org/ abs/1610.05492
[3] K. Bonawitz, V. Ivanov, B. Kreuter, A. Marcedone, H. B. McMahan, S. Patel, D. Ramage, A. Segal, and K. Seth, “Practical Secure Aggregation for Privacy-Preserving Machine Learning,” in Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. ACM, pp. 1175–1191. [Online]. Available: https://dl.acm.org/doi/10.1145/3133956.3133982
[4] P. Ramanan and K. Nakayama, “BAFFLE : Blockchain Based Aggregator Free Federated Learning,” in 2020 IEEE International Conference on Blockchain (Blockchain), pp. 72–81. [5] T. Bui, D. Cooper, J. Collomosse, M. Bell, A. Green, J. Sheridan, J. Higgins, A. Das, J. R. Keller, and O. Thereaux, “Tamper-proofing video with hierarchical attention autoencoder hashing on blockchain,” IEEE Transactions on Multimedia, vol. 22, no. 11, pp. 2858–2872, 2020.
[6] J. Xu, K. Xue, S. Li, H. Tian, J. Hong, P. Hong, and N. Yu, “Healthchain: A blockchain- based privacy preserving scheme for large-scale health data,” IEEE Internet of Things Journal, vol. 6, no. 5, pp. 8770–8781, 2019.
[7] G. Wood et al., “Ethereum: A secure decentralised generalised transaction ledger,” Ethereum project yellow paper, vol. 151, no. 2014, pp. 1–32, 2014.
[8] C. Zhang, Y. Xie, H. Bai, B. Yu, W. Li, and Y. Gao, “A survey on federated learning,” Knowledge-Based Systems, vol. 216, p. 106775, 2021.
[9] X. Jin, P.-Y. Chen, C.-Y. Hsu, C.-M. Yu, and T. Chen, “Cafe: Catastrophic data leakage in vertical federated learning,” Advances in Neural Information Processing Systems, vol. 34, pp. 994–1006, 2021.
[10] C. K. Wong, M. Gouda, and S. Lam, “Secure group communications using key graphs,” IEEE/ACM Transactions on Networking, vol. 8, no.361, pp. 16–30, 2000.
[11] A. Sherman and D. McGrew, “Key establishment in large dynamic groups using one-way function trees,” IEEE Transactions on Software Engineering, vol. 29, no. 5, pp. 444–458, 2003.
[12] H. Gu and M. Potkonjak, “Efficient and secure group key management in iot using multistage interconnected puf,” in Proceedings of the International Symposium on Low Power Electronics and Design, 2018, pp. 1–6.
[13] M. Blaze, G. Bleumer, and M. Strauss, “Divertible protocols and atomic proxy cryptography,” in International conference on the theory and applications of cryptographic techniques. Springer, 1998, pp. 127– 144.
[14] G. Ateniese, K. Fu, M. Green, and S. Hohenberger, “Improved proxy re-encryption schemes with applications to secure distributed storage,” ACM Transactions on Information and System Security (TISSEC), vol. 9, no. 1, pp. 1–30, 2006.
[15] K. O.-B. O. Agyekum, Q. Xia, E. B. Sifah, C. N. A. Cobblah, H. Xia, and J. Gao, “A proxy re-encryption approach to secure data sharing in the internet of things based on blockchain,” IEEE Systems Journal, vol. 16, no. 1, pp. 1685–1696, 2021.
[16] S. Myers and A. Shull, “Efficient hybrid proxy re-encryption for practical revocation and key rotation,” Cryptology ePrint Archive, 2017.
[17] Q. Yang, Y. Liu, T. Chen, and Y. Tong, “Federated machine learning: Concept and applications,” ACM Transactions on Intelligent Systems and Technology (TIST), vol. 10, no. 2, pp. 1–19, 2019.
[18] M. Abadi, A. Chu, I. Goodfellow, H. B. McMahan, I. Mironov, K. Talwar, and L. Zhang, “Deep learning with differential privacy,” in Proceedings of the 2016 ACM SIGSAC conference on computer and communications security, 2016, pp. 308–318.
[19] C. Dwork, A. Roth et al., “The algorithmic foundations of differential privacy,” Foundations and Trends® in Theoretical Computer Science, vol. 9, no. 3–4, pp. 211– 407, 2014.
[20] R. P. Sarode, M. Poudel, S. Shrestha, and S. Bhalla, “Blockchain for committing peer-to-peer transactions using distributed ledger technologies.” Int. J. Comput. Sci. Eng., vol. 24, no. 3, pp. 215–227, 2021.
[21] F. Casino, T. K. Dasaklis, and C. Patsakis, “A systematic literature review of blockchain-based applications: Current status, classification and open issues,” Telematics and informatics, vol. 36, pp. 55–81, 2019.
[22] V. Buterin et al., “A next-generation smart contract and decentralized application platform,” white paper, no. 3(37), 2-1, 2014.
[23] F. Victor and B. K. Luders, “Measuring ethereum-based erc20 token ̈ networks,” in International Conference on Financial Cryptography and Data Security. Springer, 2019, pp. 113–129.
[24] Z. Li, J. Kang, R. Yu, D. Ye, Q. Deng, and Y. Zhang, “Consortium blockchain for secure energy trading in industrial internet of things,” IEEE transactions on industrial informatics, vol. 14, no. 8, pp. 3690– 3700, 2017.
[25] H. G. Abreha, M. Hayajneh, and M. A. Serhani, “Federated learning in edge computing: a systematic survey,” Sensors, vol. 22, no. 2, p. 450, 2022.
[26] R. Shokri and V. Shmatikov, “Privacy-preserving deep learning,” in Proceedings of the 22nd ACM SIGSAC conference on computer and communications security, 2015, pp. 1310–1321.
[27] 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, 2013.
[28] C. Feng, B. Liu, K. Yu, S. K. Goudos, and S. Wan, “Blockchainempowered decentralized horizontal federated learning for 5g-enabled uavs,” IEEE Transactions on Industrial Informatics, vol. 18, no. 5, pp. 3582–3592, 2021.
[29] M. Mohri, G. Sivek, and A. T. Suresh, “Agnostic federated learning,” in International Conference on Machine Learning. PMLR, 2019, pp. 4615–4625.
[30] I. Hegedus, G. Danner, and M. Jelasity, “Gossip Learning as ̋ a Decentralized Alternative to Federated Learning,” in Distributed Applications and Interoperable Systems, ser. Lecture Notes in Computer Science, J. Pereira and L. Ricci, Eds. Springer International Publishing, 2019, vol. 11534, pp. 74–90. [Online]. Available: http://link.springer.com/10.1007/978-3-030-22496-7 5
[31] M. Carcary, E. Doherty, and G. Conway, “The adoption of cloud computing by irish smes an exploratory study,” Electronic Journal of Information Systems Evaluation, vol. 17, no. 1, pp. pp3–14, 2014.
[32] G. Xu, H. Li, S. Liu, K. Yang, and X. Lin, “Verifynet: Secure and verifiable federated learning,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 911– 926, 2019.
[33] Y. Aono, T. Hayashi, L. Wang, S. Moriai et al., “Privacy-preserving deep learning via additively homomorphic encryption,” IEEE Transactions on Information Forensics and Security, vol. 13, no. 5, pp. 1333–1345, 2017.
[34] B. Jia, X. Zhang, J. Liu, Y. Zhang, K. Huang, and Y. Liang, “Blockchainenabled federated learning data protection aggregation scheme with differential privacy and homomorphic encryption in iiot,” IEEE Transactions on Industrial Informatics, vol. 18, no. 6, pp. 4049–4058, 2021.
[35] H. Fang and Q. Qian, “Privacy preserving machine learning with homomorphic encryption and federated learning,” Future Internet, vol. 13, no. 4, p. 94, 2021.
[36] C. Dwork, K. Kenthapadi, F. McSherry, I. Mironov, and M. Naor, “Our Data, Ourselves: Privacy Via Distributed Noise Generation,” in Advances in Cryptology - EUROCRYPT 2006, ser. Lecture Notes in Computer Science, S. Vaudenay, Ed. Springer Berlin Heidelberg, vol. 4004, pp. 486–503. [Online]. Available: http: //link.springer.com/10.1007/11761679 29
[37] Y. Liu, J. Peng, J. Kang, A. M. Iliyasu, D. Niyato, and A. A. Abd ElLatif, “A secure federated learning framework for 5g networks,” IEEE Wireless Communications, vol. 27, no. 4, pp. 24–31, 2020.
[38] B. Balle and Y.-X. Wang, “Improving the gaussian mechanism for differential privacy: Analytical calibration and optimal denoising,” in International Conference on Machine Learning. PMLR, 2018, pp. 394–403.
[39] F. Liu, “Generalized gaussian mechanism for differential privacy,” IEEE Transactions on Knowledge and Data Engineering, vol. 31, no. 4, pp. 747–756, 2018.
[40] C. Gentry, “Fully homomorphic encryption using ideal lattices,” in Proceedings of the forty-first annual ACM symposium on Theory of computing, 2009, pp. 169–178.
[41] M. Ghadamyari and S. Samet, “Privacy-preserving statistical analysis of health data using paillier homomorphic encryption and permissioned blockchain,” in 2019 IEEE International Conference on Big Data (Big Data). IEEE, pp. 5474–5479. [Online]. Available: https: //ieeexplore.ieee.org/document/9006231/
[42] C. Jost, H. Lam, A. Maximov, and B. Smeets, “Encryption performance improvements of the paillier cryptosystem,” Cryptology ePrint Archive, 2015. [43] P. Paillier, “Public-Key Cryptosystems Based on Composite Degree Residuosity Classes,” in Advances in Cryptology — EUROCRYPT ’99, ser. Lecture Notes in Computer Science, J. Stern, Ed. Springer Berlin Heidelberg, 1999, vol. 1592, pp. 223–238. [Online]. Available: http://link.springer.com/10.1007/3-540-48910-X 16
[44] S. S. Chow, J. Weng, Y. Yang, and R. H. Deng, “Efficient unidirectional proxy re-encryption,” in International Conference on Cryptology in Africa. Springer, 2010, pp. 316–332.
[45] D. Li, Z. Luo, and B. Cao, “Blockchain-based federated learning methodologies in smart environments,” Cluster Computing, pp. 1–15, 2021.
[46] Y. Zhao, J. Zhao, L. Jiang, R. Tan, D. Niyato, Z. Li, L. Lyu, and Y. Liu, “Privacy-preserving blockchain-based federated learning for iot devices,” IEEE Internet of Things Journal, vol. 8, no. 3, pp. 1817–1829, 2020.
[47] C.-I. Fan, Y.-W. Hsu, C.-H. Shie, and Y.-F. Tseng, “Id-based multireceiver homomorphic proxy re-encryption in federated learning,” ACM Transactions on Sensor Networks (TOSN), 2022.
[48] UCI Machine Learning Repository: Iris Data Set. [Online]. Available: https://archive.ics.uci.edu/ml/datasets/iris
[49] R. A. Fisher, “The use of multiple measurements in taxonomic problems,” Annals of eugenics, vol. 7, no. 2, pp. 179–188, 1936.
[50] E. Bagdasaryan, O. Poursaeed, and V. Shmatikov, “Differential privacy has disparate impact on model accuracy,” Advances in neural information processing systems, vol. 32, 2019.
[51] L. Marchesi, M. Marchesi, G. Destefanis, G. Barabino, and D. Tigano, “Design Patterns for Gas Optimization in Ethereum,” in 2020 IEEE International Workshop on Blockchain Oriented Software Engineering (IWBOSE). IEEE, pp. 9–15. [Online]. Available: https://ieeexplore.ieee.org/document/9050163/
[52] A. Ibarrondo and A. Viand, “Pyfhel: PYthon For Homomorphic Encryption Libraries,” in Proceedings of the 9th on Workshop on Encrypted Computing & Applied Homomorphic Cryptography. ACM, 2021, pp. 11–16. [Online]. Available: https://dl.acm.org/doi/10.1145/ 3474366.3486923 |