How can multiple distributed entities collaboratively train a shared deep net on their private data while preserving privacy? This paper introduces InstaHide, a simple encryption of training images, which can be plugged into existing distributed deep learning pipelines. The encryption is efficient and applying it during training has minor effect on test accuracy. InstaHide encrypts each training image with a "one-time secret key" which consists of mixing a number of randomly chosen images and applying a random pixel-wise mask. Other contributions of this paper include: (a) Using a large public dataset (e.g. ImageNet) for mixing during its encryption, which improves security. (b) Experimental results to show effectiveness in preserving privacy against known attacks with only minor effects on accuracy. (c) Theoretical analysis showing that successfully attacking privacy requires attackers to solve a difficult computational problem. (d) Demonstrating that use of the pixel-wise mask is important for security, since Mixup alone is shown to be insecure to some some efficient attacks. (e) Release of a challenge dataset https://github.com/Hazelsuko07/InstaHide_Challenge Our code is available at https://github.com/Hazelsuko07/InstaHide

中文翻译：

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InstaHide：私有分布式学习的实例隐藏方案

多个分布式实体如何在保护隐私的同时，在他们的私人数据上协同训练共享的深度网络？本文介绍了 InstaHide，这是一种简单的训练图像加密方法，可以插入现有的分布式深度学习管道。加密是有效的，在训练期间应用它对测试准确性的影响很小。InstaHide 使用“一次性密钥”对每张训练图像进行加密，该密钥包括混合多个随机选择的图像并应用随机像素掩码。本文的其他贡献包括： (a) 在加密期间使用大型公共数据集（例如 ImageNet）进行混合，从而提高安全性。(b) 实验结果表明在保护隐私免受已知攻击方面的有效性，而对准确性的影响很小。(c) 理论分析表明成功攻击隐私需要攻击者解决一个困难的计算问题。(d) 证明使用逐像素掩码对安全性很重要，因为单独的 Mixup 被证明对一些有效的攻击是不安全的。(e) 发布挑战数据集 https://github.com/Hazelsuko07/InstaHide_Challenge 我们的代码可在 https://github.com/Hazelsuko07/InstaHide 获得