Title | ||
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PATE-AAE - Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification. |
Abstract | ||
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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. The AAE architecture allows us to obtain good synthetic speech leveraging upon a discriminative training of latent vectors. Such synthetic speech is used to build a privacy-preserving classifier when non-sensitive data is not sufficiently available in the public domain. This classifier follows the PATE scheme that uses an ensemble of noisy outputs to label the synthetic samples and guarantee $\varepsilon$-differential privacy (DP) on its derived classifiers. Our proposed framework thus consists of an AAE-based generator and a PATE-based classifier (PATE-AAE). Evaluated on the Google Speech Commands Dataset Version II, the proposed PATE-AAE improves the average classification accuracy by +$2.11\%$ and +$6.60\%$, respectively, when compared with alternative privacy-preserving solutions, namely PATE-GAN and DP-GAN, while maintaining a strong level of privacy target at $\varepsilon$=0.01 with a fixed $\delta$=10$^{-5}$. |
Year | DOI | Venue |
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2021 | 10.21437/Interspeech.2021-640 | Interspeech |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 3 |
Name | Order | Citations | PageRank |
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Chao-Han Huck Yang | 1 | 8 | 4.25 |
Sabato Marco Siniscalchi | 2 | 310 | 30.21 |
Chin-Hui Lee | 3 | 37 | 31.62 |