论文标题
一种基于像素的加密方法，用于保护隐私的深度学习模型
A Pixel-based Encryption Method for Privacy-Preserving Deep Learning Models
论文作者
论文摘要
近年来，基于像素的感知算法已成功地用于基于隐私的深度学习（DL）应用程序。但是，随后的作品中，他们的安全性通过证明选择的攻击而被打破。在本文中，我们提出了一种有效的基于像素的感知加密方法。该方法在保留原始图像的内在属性时提供了必要的安全性。因此，可以在加密域中实现深度学习（DL）应用。该方法是基于替代的，其中像素值与混乱映射产生的序列（与现有方法中使用的单个值相反）x x式。我们已经使用后勤地图来满足其低计算要求。另外，为了补偿由于有逻辑图而弥补任何效率低下的效率，我们使用第二个键来洗牌。我们已经根据DL模型的加密效率和分类精度进行了比较了提出的方法。我们已经使用CIFAR数据集验证了所提出的方法。分析表明，当在密码图像上执行分类时，该模型可以保留现有方法的准确性，同时提供了更好的安全性。
In the recent years, pixel-based perceptual algorithms have been successfully applied for privacy-preserving deep learning (DL) based applications. However, their security has been broken in subsequent works by demonstrating a chosen-plaintext attack. In this paper, we propose an efficient pixel-based perceptual encryption method. The method provides a necessary level of security while preserving the intrinsic properties of the original image. Thereby, can enable deep learning (DL) applications in the encryption domain. The method is substitution based where pixel values are XORed with a sequence (as opposed to a single value used in the existing methods) generated by a chaotic map. We have used logistic maps for their low computational requirements. In addition, to compensate for any inefficiency because of the logistic maps, we use a second key to shuffle the sequence. We have compared the proposed method in terms of encryption efficiency and classification accuracy of the DL models on them. We have validated the proposed method with CIFAR datasets. The analysis shows that when classification is performed on the cipher images, the model preserves accuracy of the existing methods while provides better security.
