计算机科学 ›› 2021, Vol. 48 ›› Issue (5): 328-333.doi: 10.11896/jsjkx.200300177
所属专题： 密码学 虚拟专题
吴少乾, 李西明
WU Shao-qian, LI Xi-ming
摘要： 生成对抗网络(Generative Adversarial Networks,GANs)自提出以来被广泛应用于各个领域。虽然在信息安全领域中对其的应用研究日益深入,但利用GANs实现公钥密码体制下的安全通信问题还未见公开报道。鉴于通信双方和敌手的对抗性质,文中利用GANs的对抗学习机制,在公钥密码体制场景下,将密钥生成器、通信双方的加解密和敌手的破译过程均作为神经网络,利用认证保密性来增强公私钥的联系,再利用对抗学习机制训练通信双方和敌手,以此实现通信双方在公开信道上的可认证加密安全通信(Authenticable Encrypted secure Communication based on Adversarial Network,AEC-AN)。实验采用了16 bit,32 bit,64 bit和128 bit长度的4种密钥进行训练,结果表明,Bob的正确率在91%~94%之间,Eve的错误率在43%~57%之间,该值接近Eve随机猜测的概率,从而证明了所提方法能够实现通信双方在敌手窃听环境下的安全通信。
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