首页> 外文会议>International Joint Conference on Neural Networks;IJCNN 2009 >Improved security of neural cryptography using don't-trust-my-partner and error prediction
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Improved security of neural cryptography using don't-trust-my-partner and error prediction

机译:使用不信任我的伙伴和错误预测提高了神经密码学的安全性

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Neural cryptography deals with the problem of key exchange using the mutual learning concept between two neural networks. The two networks will exchange their outputs (in bits) so that the key between the two communicating parties is eventually represented in the final learned weights and the two networks are said to be synchronized. Security of neural synchronization depends on the probability that an attacker can synchronize with any of the two parties during the training process, so decreasing this probability improves the reliability of exchanging their output bits through a public channel. This work proposes an exchange technique that will disrupt the attacker confidence in the exchanged outputs during training. The algorithm is based on one party sending erroneous output bits with the other party being capable of predicting and removing this error. The proposed approach is shown to outperform the synchronization with feedback algorithm in the time needed for the parties to synchronize.
机译:神经密码学使用两个神经网络之间的相互学习概念来处理密钥交换问题。这两个网络将交换其输出(以位为单位),以便最终在最终学习的权重中表示两个通信方之间的密钥,并且据说这两个网络已同步。神经同步的安全性取决于攻击者在训练过程中可以与两方中的任何一方进行同步的可能性,因此降低这种可能性可以提高通过公共通道交换其输出位的可靠性。这项工作提出了一种交换技术,它将在训练过程中破坏攻击者对交换输出的信心。该算法基于一方发送错误的输出位,而另一方能够预测并消除此错误。结果表明,在各方进行同步所需的时间内,所提出的方法优于使用反馈算法进行同步。

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