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1 results

  • 3 - Privacy Identification of Human–Generative AI Interaction

  • Edited by Dan Wu, Wuhan University, China, Shaobo Liang, Wuhan University, China
  • Book:
    Human-AI Interaction and Collaboration
    Published online:
    19 September 2025
    Print publication:
    09 October 2025, pp 43-81
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  • Summary

    Generative AI based on large language models (LLM) currently faces serious privacy leakage issues due to the wide range of parameters and diverse data sources. When using generative AI, users inevitably share data with the system. Personal data collected by generative AI may be used for model training and leaked in future outputs. The risk of private information leakage is closely related to the inherent operating mechanism of generative AI. This indirect leakage is difficult to detect by users due to the high complexity of the internal operating mechanism of generative AI. By focusing on the private information exchanged during interactions between users and generative AI, we identify the privacy dimensions involved and develop a model for privacy types in human–generative AI interactions. This can provide a reference for generative AI to avoid training private data and help it provide clear explanations of relevant content for the types of privacy users are concerned about.