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Indistinguishable Obfuscated Encryption and Decryption Based on Transformer Model

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Book cover Smart Computing and Communication (SmartCom 2022)

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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.

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Acknowledgement

This paper is supported by the National Natural Science Foundation of China (U21B2021, 62202027, 61972018, 61932014).

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Authors and Affiliations

  1. China Mobile Information Technology Company Limited, Shenzhen, China

    Pengyong Ding, Min Sun, Hong Liu, Li Li & Xin Zhang

  2. Beihang University, Beijing, China

    Zian Jin & Yizhong Liu

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  1. Pengyong Ding
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  2. Zian Jin
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  3. Yizhong Liu
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  4. Min Sun
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  5. Hong Liu
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  6. Li Li
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  7. Xin Zhang
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Corresponding author

Correspondence to Yizhong Liu .

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Editors and Affiliations

  1. Dakota State University, Madison, SD, USA

    Meikang Qiu

  2. Fudan University, Shanghai, China

    Zhihui Lu

  3. Ibaraki University, Ibaraki, Japan

    Cheng Zhang

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Ding, P. et al. (2023). Indistinguishable Obfuscated Encryption and Decryption Based on Transformer Model. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_65

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  • DOI: https://doi.org/10.1007/978-3-031-28124-2_65

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