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We propose using an adversarial autoencoder (AAE) to replace generative adversarial network (GAN) in the private aggregation of teacher ensembles (PATE), a solution for ensuring differential privacy in speech applications.
D. Dowson and B. Landau, “The fréchet distance between multivariate normal distributions,” Journal of multivariate analysis , vol. 12, no. 3, pp. 450–455, 1982
1982
Earlier work this paper cites.
C. Recommendation, “Pulse code modulation (pcm) of voice frequencies,” in ITU , 1988
1988
Earlier work this paper cites.
C. Dwork, “Differential privacy: A survey of results,” in International conference on theory and applications of models of computation . Springer, 2008, pp. 1–19
2008
Earlier work this paper cites.
C. Dwork, G. N. Rothblum, and S. Vadhan, “Boosting and differential privacy,” in 2010 IEEE 51st Annual Symposium on Foundations of Computer Science . IEEE, 2010, pp. 51–60
2010
Earlier work this paper cites.
A. Rajkumar and S. Agarwal, “A differentially private stochastic gradient descent algorithm for multiparty classification,” in Artificial Intelligence and Statistics . PMLR, 2012, pp. 933–941
2012
Earlier work this paper cites.
M. A. Pathak and B. Raj, “Privacy-preserving speaker verification and identification using gaussian mixture models,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 21, no. 2, pp. 397–406, 2012
2012
Earlier work this paper cites.
M. A. Pathak, B. Raj, S. D. Rane, and P. Smaragdis, “Privacy-preserving speech processing: cryptographic and string-matching frameworks show promise,” IEEE signal processing magazine , vol. 30, no. 2, pp. 62–74, 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in NIPS , 2014
2014
Earlier work this paper cites.
M. Fredrikson, S. Jha, and T. Ristenpart, “Model inversion attacks that exploit confidence information and basic countermeasures,” in Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security , 2015, pp. 1322–1333
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention . Springer, 2015, pp. 234–241
2015
Earlier work this paper cites.
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
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
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, p. 3152676, 2017
2017
Cited alongside, same era.
N. Papernot, M. Abadi, U. Erlingsson, I. Goodfellow, and K. Talwar, “Semi-supervised knowledge transfer for deep learning from private training data,” in ICLR , 2017
2017
Cited alongside, same era.
C. Glackin, G. Chollet, N. Dugan, N. Cannings, J. Wall, S. Tahir, I. G. Ray, and M. Rajarajan, “Privacy preserving encrypted phonetic search of speech data,” in International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2017, pp. 6414–6418
2017
Cited alongside, same era.
2017
Cited alongside, same era.
J. Chorowski, R. J. Weiss, S. Bengio, and A. van den Oord, “Unsupervised speech representation learning using wavenet autoencoders,” IEEE/ACM transactions on audio, speech, and language processing , vol. 27, no. 12, pp. 2041–2053, 2019
2019
Later among the works it cites.
S. Shatz and S. E. Chylik, “The california consumer privacy act of 2018: A sea change in the protection of california consumers’,” The Business Lawyer , vol. 75, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
2020
Later among the works it cites.
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017
2017
Cited alongside, same era.
N. Papernot, S. Song, I. Mironov, A. Raghunathan, K. Talwar, and U. Erlingsson, “Scalable private learning with pate,” in International Conference on Learning Representations , 2018
2018
Cited alongside, same era.
D. Rethage, J. Pons, and X. Serra, “A wavenet for speech denoising,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5069–5073
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
F. Brasser, T. Frassetto, K. Riedhammer, A.-R. Sadeghi, T. Schneider, and C. Weinert, “Voiceguard: Secure and private speech processing.” in Interspeech , vol. 18, 2018, pp. 1303–1307
2018
Cited alongside, same era.
K. Shmelkov, C. Schmid, and K. Alahari, “How good is my gan?” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 213–229
2018
Cited alongside, same era.
J. Jordon, J. Yoon, and M. Van Der Schaar, “Pate-gan: Generating synthetic data with differential privacy guarantees,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
H. Hu, S. M. Siniscalchi, Y. Wang, and C.-H. Lee, “Relational teacher student learning with neural label embedding for device adaptation in acoustic scene classification,” Proc. Interspeech 2020 , pp. 1196–1200, 2020
2020
Later among the works it cites.
D. Chen, T. Orekondy, and M. Fritz, “Gs-wgan: A gradient-sanitized approach for learning differentially private generators,” Neural Information Processing Systems (NeurIPS) , 2020
2020
Later among the works it cites.
K. N. Haque, R. Rana, and B. W. Schuller, “High-fidelity audio generation and representation learning with guided adversarial autoencoder,” IEEE Access , vol. 8, pp. 223 509–223 528, 2020
2020
Later among the works it cites.
2020
Later among the works it cites.
D. Dimitriadis, K. Kumatani, R. Gmyr, Y. Gaur, and S. E. Eskimez, “A federated approach in training acoustic models,” in Proc. Interspeech , 2020
2020
Later among the works it cites.
J. Qi, C.-H. H. Yang, and J. Tejedor, “Submodular rank aggregation on score-based permutations for distributed automatic speech recognition,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 3517–3521
2020
Later among the works it cites.
D. Chen, N. Yu, Y. Zhang, and M. Fritz, “Gan-leaks: A taxonomy of membership inference attacks against generative models,” in Proceedings of the 2020 ACM SIGSAC Conference on Computer and Communications Security , 2020, pp. 343–362
2020
Later among the works it cites.
C.-H. Yang, J. Qi, P.-Y. Chen, X. Ma, and C.-H. Lee, “Characterizing speech adversarial examples using self-attention u-net enhancement,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 3107–3111
2020
Later among the works it cites.