ITM Web Conf.
Volume 53, 20232nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
|Number of page(s)||11|
|Section||Ethics, Privacy and Trust, Computer Network, Big Data Systems|
|Published online||01 June 2023|
- L. Sweeney, “Achieving K-anonymity privacy protection using generalization and suppression, ” International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 10, p. 571–588, 2002. [CrossRef] [MathSciNet] [Google Scholar]
- L. Sweeney, “k-anonymity: A model for protecting privacy, ” International journal of uncertainty, fuzziness and knowledge-based systems, vol. 10, p. 557–570, 2002. [CrossRef] [MathSciNet] [Google Scholar]
- N. Li, T. Li, and S. Venkatasubramanian, “T-closeness: Privacy beyond k-anonymity and l-diversity, ” in 2007 IEEE 23rd international conference on data engineering, 2006. [Google Scholar]
- I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville and Y. Bengio, “Generative adversarial networks, ” Communications of the ACM, vol. 63, p. 139–144, 2020. [CrossRef] [Google Scholar]
- Y. Yang, Q. J. Wu and Y. Wang, “Autoencoder with invertible functions for dimension reduction and image reconstruction, ” IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 48, p. 1065–1079, 2016. [Google Scholar]
- D. P. Kingma, M. Welling and others, “An introduction to variational autoencoders, ” Foundations and Trends® in Machine Learning, vol. 12, p. 307–392, 2019. [CrossRef] [Google Scholar]
- F. Pérez-Cruz, “Kullback-Leibler divergence estimation of continuous distributions, ” in 2008 IEEE international symposium on information theory, 2008. [Google Scholar]
- I. Goodfellow, “NIPS 2016 tutorial: Generative adversarial networks, ” arXiv preprint arXiv:1701.00160, 2016. [Google Scholar]
- H. K. Khanuja and A. A. Agarkar, “Towards GAN Challenges and Its Optimal Solutions, ” Generative Adversarial Networks and Deep Learning: Theory and Applications, 2023. [Google Scholar]
- M. a. P. J. a. P. L. a. P. M. Menendez, “The Jensen-Shannon divergence, ” Journal of the Franklin Institute, vol. 334, pp. 307-318, 1997. [CrossRef] [MathSciNet] [Google Scholar]
- Q. Sun, L. Ma, S. J. Oh, L. Van Gool, B. Schiele and M. Fritz, “Natural and effective obfuscation by head inpainting, ” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018. [Google Scholar]
- H. Hukkelås, F. Lindseth and R. Mester, “Image inpainting with learnable feature imputation, ” in DAGM German Conference on Pattern Recognition, 2020. [Google Scholar]
- R. a. S. L. a. D. l. T. F. a. B. S. Gross, “Model-based face de-identification, ” in 2006 Conference on computer vision and pattern recognition workshop (CVPRW’06), 2006. [Google Scholar]
- A. Radford, L. Metz and S. Chintala, “Unsupervised representation learning with deep convolutional generative adversarial networks, ” arXiv preprint arXiv:1511.06434, 2015. [Google Scholar]
- H. Hukkelås, R. Mester and F. Lindseth, “Deepprivacy: A generative adversarial network for face anonymization, ” in International Symposium on visual computing, 2019. [Google Scholar]
- M. Arjovsky, S. Chintala and L. Bottou, “Wasserstein generative adversarial networks, ” in International conference on machine learning, 2017. [Google Scholar]
- K. Zhang, “On mode collapse in generative adversarial networks, ” in Artificial Neural Networks and Machine Learning–ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part II 30, 2021. [Google Scholar]
- Z. Ding, S. Jiang, and J. Zhao, “Take a close look at mode collapse and vanishing gradient in GAN, ” in 2022 IEEE 2nd International Conference on Electronic Technology, Communication and Information (ICETCI), 2022. [Google Scholar]
- M. Al-Rubaie and J. M. Chang, “Privacy-Preserving Machine Learning: Threats and Solutions, ” IEEE Security & Privacy, vol. 17, pp. 49-58, 2019. [CrossRef] [Google Scholar]
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