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Shi, W., Shea, R., Chen, S., Zhang, C., Jia, R. and Yu, Z. (2022) Just Fine-Tune Twice: Selective Differential Privacy for Large Language Models. In: Goldberg, Y., Kozareva, Z. and Zhang, Y., Eds., Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, 6327-6340.
https://doi.org/10.18653/v1/2022.emnlp-main.425

has been cited by the following article:

• TITLE: Whispered Tuning: Data Privacy Preservation in Fine-Tuning LLMs through Differential Privacy

  AUTHORS: Tanmay Singh, Harshvardhan Aditya, Vijay K. Madisetti, Arshdeep Bahga

  KEYWORDS: NLP, Differential Privacy, Adversarial Attacks, Informed Decisions

  JOURNAL NAME: Journal of Software Engineering and Applications, Vol.17 No.1, January 23, 2024

  ABSTRACT: The proliferation of Large Language Models (LLMs) across various sectors underscored the urgency of addressing potential privacy breaches. Vulnerabilities, such as prompt injection attacks and other adversarial tactics, could make these models inadvertently disclose their training data. Such disclosures could compromise personal identifiable information, posing significant privacy risks. In this paper, we proposed a novel multi-faceted approach called Whispered Tuning to address privacy leaks in large language models (LLMs). We integrated a PII redaction model, differential privacy techniques, and an output filter into the LLM fine-tuning process to enhance confidentiality. Additionally, we introduced novel ideas like the Epsilon Dial for adjustable privacy budgeting for differentiated Training Phases per data handler role. Through empirical validation, including attacks on non-private models, we demonstrated the robustness of our proposed solution SecureNLP in safeguarding privacy without compromising utility. This pioneering methodology significantly fortified LLMs against privacy infringements, enabling responsible adoption across sectors.