@inproceedings{kobashi-etal-2025-crypto,
    title = "Crypto-{LLM}: Two-Stage Language Model Pre-training with Ciphered and Natural Language Data",
    author = "Kobashi, Yohei  and
      Uchiyama, Fumiya  and
      Kojima, Takeshi  and
      Gambardella, Andrew  and
      Cao, Qi  and
      Iwasawa, Yusuke  and
      Matsuo, Yutaka",
    editor = "Inui, Kentaro  and
      Sakti, Sakriani  and
      Wang, Haofen  and
      Wong, Derek F.  and
      Bhattacharyya, Pushpak  and
      Banerjee, Biplab  and
      Ekbal, Asif  and
      Chakraborty, Tanmoy  and
      Singh, Dhirendra Pratap",
    booktitle = "Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics",
    month = dec,
    year = "2025",
    address = "Mumbai, India",
    publisher = "The Asian Federation of Natural Language Processing and The Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-ijcnlp-aacl/2025.ijcnlp-long.135/",
    pages = "2502--2520",
    ISBN = "979-8-89176-298-5",
    abstract = "As the adoption of large language models (LLMs) continues to grow, the risk of sensitive data leakage from their training datasets has become a critical concern. This study proposes a novel method for encrypting training data using a polyalphabetic substitution cipher. This approach prevents the model from learning sensitive information while allowing it to capture abstract linguistic patterns. We pre-trained a Llama 3 model (551M parameters) using approximately 7.5 billion tokens of encrypted data and subsequently conducted continual pre-training with another 2.5 billion tokens of plaintext data. The effectiveness of the model was evaluated by comparing its downstream task performance with a model trained solely on plaintext data. In addition, we evaluated the risk of sensitive data leakage through name reconstruction, true-prefix and data extraction attacks. These results demonstrate the potential of our approach to balance data security with model performance."
}