论文标题
生成具有隐私保证的高保真合成数据集
Generating Higher-Fidelity Synthetic Datasets with Privacy Guarantees
论文作者
论文摘要
本文考虑了通过将真实数据替换为样本形成生成性的对抗网络，从而在通用机器学习开发任务（例如数据注释和检查）中增强用户隐私的问题。我们建议采用贝叶斯差异隐私作为获得严格的理论保证的手段，同时提供更好的隐私 - 私人权衡权衡。我们通过实验证明，与先前的工作相比，我们的方法会产生更高的获取样本，以（1）检测到更多微妙的数据误差和偏见，以及（2）直接在人工样本上训练时，通过实现高精度来减少对真实数据标记的需求。
This paper considers the problem of enhancing user privacy in common machine learning development tasks, such as data annotation and inspection, by substituting the real data with samples form a generative adversarial network. We propose employing Bayesian differential privacy as the means to achieve a rigorous theoretical guarantee while providing a better privacy-utility trade-off. We demonstrate experimentally that our approach produces higher-fidelity samples, compared to prior work, allowing to (1) detect more subtle data errors and biases, and (2) reduce the need for real data labelling by achieving high accuracy when training directly on artificial samples.
