# PATE-AAE: Incorporating Adversarial Autoencoder into Private Aggregation of Teacher Ensembles for Spoken Command Classification

arXiv: https://arxiv.org/abs/2104.01271 · HTML: https://mlsanity.com/paper/2104.01271/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 41 references that are in the map.
- First posted on arXiv 2 Apr 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Extracting Training Data from Large Language Models](https://mlsanity.com/paper/2012.07805/) (2020)
- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Adversarial Autoencoders](https://mlsanity.com/paper/1511.05644/) (2015)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Scalable Private Learning with PATE](https://mlsanity.com/paper/1802.08908/) (2018)
- [Differentially Private Generative Adversarial Network](https://mlsanity.com/paper/1802.06739/) (2018)
- [GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators](https://mlsanity.com/paper/2006.08265/) (2020)
- [Federated Learning for Keyword Spotting](https://mlsanity.com/paper/1810.05512/) (2018)
- [Unsupervised speech representation learning using WaveNet autoencoders](https://mlsanity.com/paper/1901.08810/) (2019)
- [Federated Quantum Machine Learning](https://mlsanity.com/paper/2103.12010/) (2021)
- [How good is my GAN?](https://mlsanity.com/paper/1807.09499/) (2018)
- [Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition](https://mlsanity.com/paper/2010.13309/) (2020)
- [A Wavenet for Speech Denoising](https://mlsanity.com/paper/1706.07162/) (2017)
- [Characterizing Speech Adversarial Examples Using Self-Attention U-Net Enhancement](https://mlsanity.com/paper/2003.13917/) (2020)
- [Guidelines for Implementing and Auditing Differentially Private Systems](https://mlsanity.com/paper/2002.04049/) (2020)
- [High-Fidelity Audio Generation and Representation Learning with Guided Adversarial Autoencoder](https://mlsanity.com/paper/2006.00877/) (2020)
- [Relational Teacher Student Learning with Neural Label Embedding for Device Adaptation in Acoustic Scene Classification](https://mlsanity.com/paper/2008.00110/) (2020)
- [Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition](https://mlsanity.com/paper/2001.10529/) (2020)

## Built on by (most cited first, 10 of 10)

- [Voice2Series: Reprogramming Acoustic Models for Time Series Classification](https://mlsanity.com/paper/2106.09296/) (2021)
- [When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing](https://mlsanity.com/paper/2203.03550/) (2022)
- [Longer Version for "Deep Context-Encoding Network for Retinal Image Captioning"](https://mlsanity.com/paper/2105.14538/) (2021)
- [Mitigating Closed-model Adversarial Examples with Bayesian Neural Modeling for Enhanced End-to-End Speech Recognition](https://mlsanity.com/paper/2202.08532/) (2022)
- [In Differential Privacy, There is Truth: On Vote Leakage in Ensemble Private Learning](https://mlsanity.com/paper/2209.10732/) (2022)
- [Robust Unsupervised Multi-Object Tracking in Noisy Environments](https://mlsanity.com/paper/2105.10005/) (2021)
- [An Experimental Study on Private Aggregation of Teacher Ensemble Learning for End-to-End Speech Recognition](https://mlsanity.com/paper/2210.05614/) (2022)
- [Differentially Private Adapters for Parameter Efficient Acoustic Modeling](https://mlsanity.com/paper/2305.11360/) (2023)
- [An Ensemble Teacher-Student Learning Approach with Poisson Sub-sampling to Differential Privacy Preserving Speech Recognition](https://mlsanity.com/paper/2210.06382/) (2022)
- [Private Multi-Winner Voting for Machine Learning](https://mlsanity.com/paper/2211.15410/) (2022)

## Abstract (arXiv)

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 δ=10-5.