@inproceedings{c4469285a9254fb2ad674991d30449ea,
title = "Indistinguishable Obfuscated Encryption and Decryption Based on Transformer Model",
abstract = "To solve the problem in secure encryption in cryptography, indistinguishability Obfuscation (iO) was born. It is a crypto-complete idea, based on which we can build many cryptographic construction. The implementation of it can hide both the dataset and the program itself. In this paper, we use the idea of translation in the (Natural Language Processing) NLP-like language model to realize the conversion between plaintexts and ciphertexts with the help of hints. We trained a self-attention transformer model, successfully hiding the dataset as well as the encryption and decryption programs. The input of the encryption model is a plaintext prefixed with a hint and the output is the result of encryption using one of the specified algorithms. The input and output of the decryption model are the opposite of the encryption one.",
keywords = "Blockchain, Indistinguishable Confusion, Information Security, Transformer",
author = "Pengyong Ding and Zian Jin and Yizhong Liu and Min Sun and Hong Liu and Li Li and Xin Zhang",
note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 7th International Conference on Smart Computing and Communication, SmartCom 2022 ; Conference date: 18-11-2022 Through 20-11-2022",
year = "2023",
doi = "10.1007/978-3-031-28124-2\_65",
language = "英语",
isbn = "9783031281235",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "685--694",
editor = "Meikang Qiu and Zhihui Lu and Cheng Zhang",
booktitle = "Smart Computing and Communication - 7th International Conference, SmartCom 2022, Proceedings",
address = "德国",
}