论文标题
对隐私神经网络的图像加密方案的攻击
Attacks on Image Encryption Schemes for Privacy-Preserving Deep Neural Networks
论文作者
论文摘要
隐私保存机器学习是一个积极的研究领域，通常依赖于诸如同态加密或安全多方计算等技术。最近，田中和Sirichotedumrong，Kinoshita和Kiya提出了最近使用深层神经网进行机器学习的新型加密技术。我们提出了针对这两种提出的图像加密方案的新选择的plaintext和仅密文的攻击，并证明了攻击对几个示例的有效性。
Privacy preserving machine learning is an active area of research usually relying on techniques such as homomorphic encryption or secure multiparty computation. Recent novel encryption techniques for performing machine learning using deep neural nets on images have recently been proposed by Tanaka and Sirichotedumrong, Kinoshita, and Kiya. We present new chosen-plaintext and ciphertext-only attacks against both of these proposed image encryption schemes and demonstrate the attacks' effectiveness on several examples.
