We propose a privacy-preserving machine learning scheme with encryption-then-compression (EtC) images, where EtC images are images encrypted by using a block-based encryption method proposed for EtC systems with JPEG compression. In this paper, a novel property of EtC images is first discussed, although EtC ones was already shown to be compressible as a property. The novel property allows us to directly apply EtC images to machine learning algorithms non-specialized for computing encrypted data. In addition, the proposed scheme is demonstrated to provide no degradation in the performance of some typical machine learning algorithms including the support vector machine algorithm with kernel trick and random forests under the use of z-score normalization. A number of facial recognition experiments with are carried out to confirm the effectiveness of the proposed scheme.
翻译:我们提出一个使用加密时压缩图像的隐私保护机学习方案,其中EtC图像是使用为EtC系统提议的基于块的加密方法,用JPEG压缩来加密的图像。在本文中,首先讨论EtC图像的新属性,尽管EtC图像已经显示是一种可压缩的属性。新属性使我们能够直接将EtC图像应用到计算机加密数据不专门使用的机器学习算法中。此外,还演示了拟议办法,以不降低某些典型机器学习算法的性能,包括使用核子正常化下的内核戏法和随机森林支持矢量机算法。还进行了一些面部识别实验,以确认拟议办法的有效性。