Machine Learning Cryptanalysis of a Quantum Random Number Generator
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2019
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| _version_ | 1866910930213797888 |
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| author | Truong, Nhan Duy Haw, Jing Yan Assad, Syed Muhamad Lam, Ping Koy Kavehei, Omid |
| author_facet | Truong, Nhan Duy Haw, Jing Yan Assad, Syed Muhamad Lam, Ping Koy Kavehei, Omid |
| contents | Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1905_02342 |
| institution | arXiv |
| publishDate | 2019 |
| record_format | arxiv |
| spellingShingle | Machine Learning Cryptanalysis of a Quantum Random Number Generator Truong, Nhan Duy Haw, Jing Yan Assad, Syed Muhamad Lam, Ping Koy Kavehei, Omid Machine Learning Cryptography and Security Quantum Physics Random number generators (RNGs) that are crucial for cryptographic applications have been the subject of adversarial attacks. These attacks exploit environmental information to predict generated random numbers that are supposed to be truly random and unpredictable. Though quantum random number generators (QRNGs) are based on the intrinsic indeterministic nature of quantum properties, the presence of classical noise in the measurement process compromises the integrity of a QRNG. In this paper, we develop a predictive machine learning (ML) analysis to investigate the impact of deterministic classical noise in different stages of an optical continuous variable QRNG. Our ML model successfully detects inherent correlations when the deterministic noise sources are prominent. After appropriate filtering and randomness extraction processes are introduced, our QRNG system, in turn, demonstrates its robustness against ML. We further demonstrate the robustness of our ML approach by applying it to uniformly distributed random numbers from the QRNG and a congruential RNG. Hence, our result shows that ML has potentials in benchmarking the quality of RNG devices. |
| title | Machine Learning Cryptanalysis of a Quantum Random Number Generator |
| topic | Machine Learning Cryptography and Security Quantum Physics |
| url | https://arxiv.org/abs/1905.02342 |