Generative Trapdoors for Public Key Cryptography Based on Automatic Entropy Optimization  

作　　者：Shuaishuai Zhu Yiliang Han 

机构地区：[1]College of Cryptography Engineering,Engineering University of People’s Armed Police,Xi’an 710086,China [2]Key Laboratory of Network and Information Security under the People’s Armed Police,Xi’an 710086,China

出　　处：《China Communications》2021年第8期35-46,共12页中国通信（英文版）

基　　金：the National Natural Science Foundation of China(No.61572521,U1636114);National Key Project of Research and Development Plan(2017YFB0802000);Natural Science Foundation of Shaanxi Province(2021JM-252);Innovative Research Team Project of Engineering University of APF(KYTD201805);Fundamental Research Project of Engineering University of PAP(WJY201910).

摘　　要：Trapdoor is a key component of public key cryptography design which is the essential security foundation of modern cryptography.Normally,the traditional way in designing a trapdoor is to identify a computationally hard problem,such as the NPC problems.So the trapdoor in a public key encryption mechanism turns out to be a type of limited resource.In this paper,we generalize the methodology of adversarial learning model in artificial intelligence and introduce a novel way to conveniently obtain sub-optimal and computationally hard trapdoors based on the automatic information theoretic search technique.The basic routine is constructing a generative architecture to search and discover a probabilistic reversible generator which can correctly encoding and decoding any input messages.The architecture includes a trapdoor generator built on a variational autoencoder(VAE)responsible for searching the appropriate trapdoors satisfying a maximum of entropy,a random message generator yielding random noise,and a dynamic classifier taking the results of the two generator.The evaluation of our construction shows the architecture satisfying basic indistinguishability of outputs under chosen-plaintext attack model(CPA)and high efficiency in generating cheap trapdoors.

关 键 词：generative model public key encryption indistinguishability model security model deep learning 

分 类 号：TN918.1[电子电信—通信与信息系统]