A privacy preservation framework for feedforward-designed convolutional neural networks

Author:

Li De,Wang JinyanORCID,Li Qiyu,Hu Yuhang,Li Xianxian

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference55 articles.

1. Abadi, M., Chu, A., Goodfellow, I. J., McMahan, H. B., Mironov, I., & Talwar, K., et al. (2016). Deep Learning with Differential Privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security (pp. 308–318).

2. Chaudhuri, K., & Monteleoni, C. (2008). Privacy-preserving logistic regression. In: Proceedings of the twenty-second annual conference on neural information processing systems (pp. 289–296).

3. PixelHop: A successive subspace learning (SSL) method for object recognition;Chen;Journal of Visual Communication and Image Representation,2020

4. DefakeHop: A light-weight high-performance deepfake detector;Chen,2021

5. Pixelhop++: A small successive-subspace-learning-based (ssl-based) model for image classification;Chen,2020

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