içgörü - Machine Learning - # Privacy-Preserving GAN Training

Training Generative Models from Privatized Data via Entropic Optimal Transport

Q: How does the proposed framework compare to existing methods for training GANs on privately collected data

The proposed framework for training GANs on privately collected data stands out from existing methods in several key aspects. Firstly, it leverages entropic optimal transport to enable the generator to learn the raw (unprivatized) data distribution even when only privatized samples are available. This unique approach allows for accurate population-level model extraction from noisy and privatized data, addressing a common challenge in privacy-preserving machine learning. Unlike traditional methods that focus on adding noise during the training phase or using differential privacy mechanisms, this framework operates at the data level by incorporating entropic regularization of optimal transport. By tailoring the cost function to match the privatization mechanism, it ensures fast convergence rates and statistical guarantees while mitigating both the effects of noise introduced by privatization and overcoming dimensionality challenges in statistical convergence. Furthermore, the framework provides a novel way to train GANs on differentially privatized user data without compromising accuracy or utility. It offers a seamless transition between non-privatized and privatized settings, making it particularly suitable for scenarios like federated learning where users can locally privatize their data before sharing it with service providers.

Q: What are potential implications of using entropic optimal transport in other machine learning tasks beyond GAN training

The findings from this study have broader implications beyond GAN training and could potentially be applied to various other machine learning tasks. One potential application is in image denoising tasks where sensitive images need to be processed while preserving privacy. By utilizing entropic optimal transport techniques as demonstrated in this study, researchers could develop more robust and efficient models for denoising images without compromising individual privacy. Additionally, these findings could also be extended to natural language processing tasks such as text generation or translation where maintaining privacy is crucial. The use of entropic regularization of optimal transport could help improve model performance while ensuring that sensitive textual information remains protected through differential privacy mechanisms. Overall, integrating entropic optimal transport into different machine learning applications has the potential to enhance both performance and privacy preservation across various domains beyond just GAN training.

Q: How can the findings in this study be applied to real-world scenarios involving sensitive or private datasets

The insights gained from this study can have significant implications for real-world scenarios involving sensitive or private datasets: Healthcare Data: In healthcare settings where patient confidentiality is paramount, applying similar frameworks can enable researchers to train generative models on medical records while protecting patient privacy through differential privacy mechanisms. This approach would allow for valuable insights to be extracted from healthcare data without compromising individual confidentiality. Financial Data: When dealing with financial transactions or customer information that needs protection against unauthorized access, leveraging entropic optimal transport techniques can aid in developing secure yet effective models for fraud detection or risk assessment without exposing sensitive details. Government Datasets: Government agencies handling large volumes of confidential information could benefit from implementing these methodologies when analyzing public sector datasets securely. By incorporating differential privacy measures alongside advanced machine learning algorithms based on entropic regularization of optimal transport, they can ensure compliance with strict regulations while still deriving meaningful insights. By applying these research findings ethically and responsibly across diverse sectors dealing with private datasets, organizations can unlock new possibilities for innovation while upholding stringent standards of data protection and security protocols within their operations."

Temel Kavramlar

The authors propose a framework for training Generative Adversarial Networks (GANs) on differentially privatized data using entropic optimal transport, enabling the generator to learn the raw data distribution even with access to privatized samples.

Özet

The paper introduces a novel approach to training GANs on locally privatized user data, showcasing the effectiveness of entropic optimal transport in mitigating privacy noise and facilitating convergence. Experimental results demonstrate the efficacy of the proposed method in learning from privatized data.

Local differential privacy is discussed as a powerful method for privacy-preserving data collection, emphasizing the importance of rethinking machine learning methods for accurate model extraction from noisy, privatized data. The study explores training generative models from locally privatized user data, focusing on the problem of learning accurate population-level models from noisy samples. The paper highlights the significance of entropic regularization of optimal transport in enabling GANs to recover original distributions from privatized samples efficiently.

The research provides theoretical insights into convergence guarantees for locally differential private frameworks with entropic optimal transport, showcasing how this approach uniquely mitigates privacy noise and dimensionality challenges. Empirical validation supports the theoretical contributions, demonstrating superior performance in practical scenarios.

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arxiv.org

İstatistikler

We prove similar convergence guarantees for both p = 1 and p = 2.
Laplace or Gaussian mechanism noise scale σ > c + √(c^2 + ϵ)/ϵ√2∆.
E(G(Z),Y )∼π[− log pM(Y | U)] + Iπ(U,Y).
Sc(PG(Z), PY) = inf π ∈ P E(U,Y)∼π[c(U,Y)] + Iπ(U,Y).

Alıntılar

"We show that entropic regularization uniquely enables mitigation of both effects of privatization noise and curse of dimensionality."
"Entropic regularization facilitates rapid convergence of GANs and circumvents curse of dimensionality."

Önemli Bilgiler Şuradan Elde Edildi

Training generative models from privatized data

by Dari... : arxiv.org 03-04-2024

https://arxiv.org/pdf/2306.09547.pdf

Training generative models from privatized data

Daha Derin Sorular

How does the proposed framework compare to existing methods for training GANs on privately collected data

The proposed framework for training GANs on privately collected data stands out from existing methods in several key aspects. Firstly, it leverages entropic optimal transport to enable the generator to learn the raw (unprivatized) data distribution even when only privatized samples are available. This unique approach allows for accurate population-level model extraction from noisy and privatized data, addressing a common challenge in privacy-preserving machine learning.
Unlike traditional methods that focus on adding noise during the training phase or using differential privacy mechanisms, this framework operates at the data level by incorporating entropic regularization of optimal transport. By tailoring the cost function to match the privatization mechanism, it ensures fast convergence rates and statistical guarantees while mitigating both the effects of noise introduced by privatization and overcoming dimensionality challenges in statistical convergence.
Furthermore, the framework provides a novel way to train GANs on differentially privatized user data without compromising accuracy or utility. It offers a seamless transition between non-privatized and privatized settings, making it particularly suitable for scenarios like federated learning where users can locally privatize their data before sharing it with service providers.

What are potential implications of using entropic optimal transport in other machine learning tasks beyond GAN training

The findings from this study have broader implications beyond GAN training and could potentially be applied to various other machine learning tasks.
One potential application is in image denoising tasks where sensitive images need to be processed while preserving privacy. By utilizing entropic optimal transport techniques as demonstrated in this study, researchers could develop more robust and efficient models for denoising images without compromising individual privacy.
Additionally, these findings could also be extended to natural language processing tasks such as text generation or translation where maintaining privacy is crucial. The use of entropic regularization of optimal transport could help improve model performance while ensuring that sensitive textual information remains protected through differential privacy mechanisms.
Overall, integrating entropic optimal transport into different machine learning applications has the potential to enhance both performance and privacy preservation across various domains beyond just GAN training.

How can the findings in this study be applied to real-world scenarios involving sensitive or private datasets

The insights gained from this study can have significant implications for real-world scenarios involving sensitive or private datasets:

Healthcare Data: In healthcare settings where patient confidentiality is paramount, applying similar frameworks can enable researchers to train generative models on medical records while protecting patient privacy through differential privacy mechanisms. This approach would allow for valuable insights to be extracted from healthcare data without compromising individual confidentiality.

Financial Data: When dealing with financial transactions or customer information that needs protection against unauthorized access, leveraging entropic optimal transport techniques can aid in developing secure yet effective models for fraud detection or risk assessment without exposing sensitive details.

Government Datasets: Government agencies handling large volumes of confidential information could benefit from implementing these methodologies when analyzing public sector datasets securely. By incorporating differential privacy measures alongside advanced machine learning algorithms based on entropic regularization of optimal transport, they can ensure compliance with strict regulations while still deriving meaningful insights.

By applying these research findings ethically and responsibly across diverse sectors dealing with private datasets, organizations can unlock new possibilities for innovation while upholding stringent standards of data protection and security protocols within their operations."