Einblick - Data Privacy - # DP Synthetic Data Generation

Differentially Private Synthetic Data Generation via Foundation Model APIs for Images

Q: How can PE address privacy concerns related to pre-training data of foundation models

PE can address privacy concerns related to the pre-training data of foundation models by ensuring that the private data used in the PE algorithm has no overlap with the pre-training data of the foundation models. This is crucial for maintaining privacy and preventing any potential breaches or leaks of sensitive information. By using APIs from blackbox models, which do not reveal their training datasets, users can run PE safely as long as they ensure that their private data has never been shared or posted online. For local models where users have full control over model weights and architectures, they can pre-train the models on non-overlapping data to guarantee privacy.

Q: What are the implications of using APIs from blackbox models versus local models in terms of privacy and liability

The implications of using APIs from blackbox models versus local models in terms of privacy and liability are significant. When using APIs from blackbox models, it is safer to consider private data that has never been shared online to prevent any potential overlaps with pre-training data. This ensures better protection of user privacy and reduces liability risks associated with unintentional exposure of sensitive information. On the other hand, when using APIs from local models where users have full control over model weights and architectures, they can take additional precautions to ensure no overlap between private and pre-training data for enhanced security.

Q: How can PE be extended to other data modalities beyond images for generating privacy-preserving synthetic data

To extend PE to other data modalities beyond images for generating privacy-preserving synthetic data, similar principles can be applied but tailored to suit different types of datasets. For text-based modalities, PE could utilize language generation APIs while ensuring differential privacy in text synthesis processes. In tabular or time series datasets, PE could leverage API functionalities specific to those domains while maintaining DP guarantees throughout the synthetic data generation process. Adapting PE's framework for diverse datatypes would involve customizing distance functions and fitness metrics based on each modality's unique characteristics while upholding stringent privacy standards across all implementations.

Kernkonzepte

Generating differentially private synthetic data using foundation model APIs without training.

Zusammenfassung

この論文は、異なるプライベートデータに似た合成データを生成する方法に焦点を当てています。APIを使用してトレーニングせずに差分プライバシーの合成データを生成する手法であるPrivate Evolution（PE）フレームワークを提案しました。PEは、公開された大規模なファウンデーションモデルを活用して高解像度の画像データセットを扱うことが可能であり、既存のSOTAトレーニングベースの手法よりも優れた結果を示すことができます。さらに、PEはAPIアクセスのみで無制限の有用なサンプルを生成する能力も持っています。

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aus dem Quellinhalt

Quelle besuchen

arxiv.org

Statistiken

CIFAR10（ImageNetが公開データ）でFID≤7.9、プライバシーコストϵ=0.67を達成。
Camelyon17（乳癌転移の分類用医療データセット）でDP合成バージョン（ε=7.58）作成。
100枚の512x512画像から構成される高解像度データセットでも効果的な結果を達成。

Zitate

"Generating differentially private (DP) synthetic data that closely resembles the original private data is a scalable way to mitigate privacy concerns in the current data-driven world."
"PE can match or even outperform state-of-the-art (SOTA) methods without any model training."
"We show that PE not only has the potential to be realized, but also to match or improve SOTA training-based DP synthetic image algorithms despite more restrictive model access."

Wichtige Erkenntnisse aus

Differentially Private Synthetic Data via Foundation Model APIs 1

by Zinan Lin,Si... um arxiv.org 03-01-2024

https://arxiv.org/pdf/2305.15560.pdf

Differentially Private Synthetic Data via Foundation Model APIs 1

Tiefere Fragen

How can PE address privacy concerns related to pre-training data of foundation models

PE can address privacy concerns related to the pre-training data of foundation models by ensuring that the private data used in the PE algorithm has no overlap with the pre-training data of the foundation models. This is crucial for maintaining privacy and preventing any potential breaches or leaks of sensitive information. By using APIs from blackbox models, which do not reveal their training datasets, users can run PE safely as long as they ensure that their private data has never been shared or posted online. For local models where users have full control over model weights and architectures, they can pre-train the models on non-overlapping data to guarantee privacy.

What are the implications of using APIs from blackbox models versus local models in terms of privacy and liability

The implications of using APIs from blackbox models versus local models in terms of privacy and liability are significant. When using APIs from blackbox models, it is safer to consider private data that has never been shared online to prevent any potential overlaps with pre-training data. This ensures better protection of user privacy and reduces liability risks associated with unintentional exposure of sensitive information. On the other hand, when using APIs from local models where users have full control over model weights and architectures, they can take additional precautions to ensure no overlap between private and pre-training data for enhanced security.

How can PE be extended to other data modalities beyond images for generating privacy-preserving synthetic data

To extend PE to other data modalities beyond images for generating privacy-preserving synthetic data, similar principles can be applied but tailored to suit different types of datasets. For text-based modalities, PE could utilize language generation APIs while ensuring differential privacy in text synthesis processes. In tabular or time series datasets, PE could leverage API functionalities specific to those domains while maintaining DP guarantees throughout the synthetic data generation process. Adapting PE's framework for diverse datatypes would involve customizing distance functions and fitness metrics based on each modality's unique characteristics while upholding stringent privacy standards across all implementations.