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NTIS 바로가기Journal of the convergence on culture technology : JCCT = 문화기술의 융합, v.10 no.1, 2024년, pp.435 - 441
In order for the large amount of collected data sets to be used as deep learning training data, sensitive personal information such as resident registration number and disease information must be changed or encrypted to prevent it from being exposed to hackers, and the data must be reconstructed to ...
W. Wei and L. Liu, "Gradient Leakage Attack?Resilient Deep Learning," IEEE Transactions on?Information Forensics and Security, Vol. 17, pp.?303-316,2022(DOI:10.1109/TIFS.2021.3139777)
N. Bugshan, I. Khalil, M. S. Rahman, M.?Atiquzzaman;X. Yi, S. Badsha, "Toward?Trustworthy and Privacy-Preserving Federated?Deep Learning Service Framework for Industrial?Internet of Things," IEEE Transactions on?Industrial Informatics, Vol. 19, No.2 pp.?1535-1547,2023(DOI: 10.1109/TII.2022.3209200)
D. Mistry, M. F. Mridha, Me. Safran, S. Alfarhood,?A. K. Saha, D. Che, "Privacy-Preserving?On-Screen Activity Tracking and Classification in?E-Learning Using Federated Learning," IEEE?Access, Vol. 11, pp. 79315-79329, 2023(DOI:?10.1109/ACCESS.2023.3299331)
I. Fontana, M. Langheinrich, M. Gjoreski, "GANs?for Privacy-Aware Mobility Modeling," IEEE?Access, Vol. 11, pp. 29250-29262, 2023(DOI:10.1109/ACCESS.2023.3260981)
R. Podschwadt, D. Takabi, P. Hu, M. H. Rafiei, Z.?Cai, "A Survey of Deep Learning Architectures for?Privacy-Preserving Machine Learning With Fully?Homomorphic Encryption," IEEE Access, Vol. 10,?pp.117477-117500,2022(DOI:10.1109/ACCESS.2022.3219049)
T. Zhang, T. Zhu, K. Gao, W. Zhou, P. S. Yu,?"Balancing Learning Model Privacy, Fairness, and?Accuracy With Early Stopping Criteria," IEEE?Transactions on Neural Networks and Learning?Systems, Vol. 34, No.9, pp.5557-5569,2023(DOI:?10.1109/TNNLS.2021.3129592)
V. Stephanie, I. Khalil, M. S. Rahman, M.?Atiquzzaman, "Privacy-Preserving Ensemble?Infused Enhanced Deep Neural Network?Framework for Edge Cloud Convergence," IEEE?Internet of Things Journal, Vol. 10, No.5,?pp.3763-3773,2023(DOI: 10.1109/JIOT.2022.3151982)
M. Shateri, F. Messina, P. Piantanida, F. Labeau,?"Privacy-Cost Management in Smart Meters With?Mutual-Information-BasedReinforcement Learning,"?IEEE Internet of Things Journal, Vol. 9,?No.22,pp.22389-22398,2022(DOI:10.1109/JIOT.2021.3128488)
Z. Wu, H. Wang, Z. Wang, H. Jin, Z. Wang,?"Privacy-Preserving Deep Action Recognition: An?Adversarial Learning Framework and A New?Dataset," IEEE Transactions on Pattern Analysis?and Machine Intelligence, Vol. 44, No. 4, pp.?2126-2139,2022(DOI: 10.1109/TPAMI.2020.3026709)
L. Xiang, W. Li, J. Yang, X. Wang, B. Li,?"Differentially-Private Deep Learning With?Directional Noise," IEEE Transactions on Mobile?Computing, Vol. 22, No. 5, pp. 2599-2612,2023(DOI:10.1109/TMC.2021.3130060)
A. E. Ouadrhiri, A. Abdelhadi, "Differential Privacy?for Deep and Federated Learning: A Survey," IEEE?Access, Vol. 10, pp. 22359-22380,2022(DOI:10.1109/ACCESS.2022.3151670)
R. Parekh, N. Patel, R. Gupta, N. K. Jadav, S.?Tanwar, A. Alharbi, A. Tolba, B.-C. Neagu, M. S.?Raboaca, "GeFL: Gradient Encryption-Aided?Privacy Preserved Federated Learning for?Autonomous Vehicles," IEEE Access, Vol. 11, pp.?1825-1839,2023(DOI:10.1109/ACCESS.2023.3233983)
W. Zhang, B. Jiang, M. Li, X. Lin,?"Privacy-Preserving Aggregate Mobility Data?Release: An Information-Theoretic Deep?Reinforcement Learning Approach," IEEE?Transactions on Information Forensics and?Security, Vol. 17, pp. 849-864, 2022(DOI:10.1109/TIFS.2022.3152361)
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