IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization
<|reference_start|>IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization: In this work, we address the problem of text anonymization where the goal is to prevent adversaries from correctly inferring private attributes of the author, while keeping the text utility, i.e., meaning and semantics. We propose IncogniText, a technique that anonymizes the text to mislead a potential adversary into predicting a wrong private attribute value. Our empirical evaluation shows a reduction of private attribute leakage by more than 90%. Finally, we demonstrate the maturity of IncogniText for real-world applications by distilling its anonymization capability into a set of LoRA parameters associated with an on-device model.<|reference_end|>
@article{frikha2024incognitext:,
    title={IncogniText: Privacy-enhancing Conditional Text Anonymization via
  LLM-based Private Attribute Randomization},
    author={Ahmed Frikha, Nassim Walha, Krishna Kanth Nakka, Ricardo Mendes, Xue
  Jiang, Xuebing Zhou},
    journal={arXiv preprint arXiv:2407.02956},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.02956},
    primaryClass={cs.CR cs.AI cs.CL cs.LG}
}