In the era of big data, deep learning has become an increasingly popular topic. It has outstanding achievements in the fields of image recognition, object detection, and natural language processing et al. The first priority of deep learning is exploiting valuable information from a large amount of data, which will inevitably induce privacy issues that are worthy of attention. Presently, several privacy-preserving deep learning methods have been proposed, but most of them suffer from a non-negligible degradation of either efficiency or accuracy. Negative database (\textit{NDB}) is a new type of data representation which can protect data privacy by storing and utilizing the complementary form of original data. In this paper, we propose a privacy-preserving deep learning method named NegDL based on \textit{NDB}. Specifically, private data are first converted to \textit{NDB} as the input of deep learning models by a generation algorithm called \textit{QK}-hidden algorithm, and then the sketches of \textit{NDB} are extracted for training and inference. We demonstrate that the computational complexity of NegDL is the same as the original deep learning model without privacy protection. Experimental results on Breast Cancer, MNIST, and CIFAR-10 benchmark datasets demonstrate that the accuracy of NegDL could be comparable to the original deep learning model in most cases, and it performs better than the method based on differential privacy.

翻译：在海量数据时代,深层次学习已成为一个越来越受欢迎的话题。在图像识别、物体探测和自然语言处理等领域,深层次学习取得了杰出成就。深层次学习的第一优先事项是利用大量数据提供的宝贵信息,这不可避免地会引起值得注意的隐私问题。目前,提出了若干保护隐私的深层次学习方法,但大多数方法都存在效率或准确性不可忽略的退化。负值数据库(\ textit{NDB})是一种新型的数据代表,可以通过储存和使用原始数据的补充形式来保护数据隐私。在本文中,我们提出一个名为NegDL的隐私保护深层学习方法,根据\ textitit{NDB} 以NegDL 为基础。具体地说,私人数据首先转换为textit{NDB},作为代算法的深层次学习模型,称为\ textitilitle{K}-hiden 算法,然后为培训与推导。我们证明,NegDL的计算复杂度深层次的深层次学习方法是NegDL的最深层的精确性研究方法,而测试的原始的精确性记录和CAR的原始数据是没有原始的原始学习模式。