Privacy Auditing of Large Language Models: Advancements in Canary Design

In the evolving landscape of artificial intelligence, ensuring the privacy of data processed by large language models (LLMs) has become paramount. The paper "Privacy Auditing of Large Language Models" by Ashwinee Panda et al. introduces a novel approach to privacy auditing, focusing on the development of more effective canaries to detect potential data leakage. :contentReference[oaicite:4]{index=4}:contentReference[oaicite:5]{index=5}

1. Limitations of Existing Privacy Auditing Techniques

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2. Innovative Canary Generation Methodology

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3. Empirical Evaluation and Results

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4. Implications for Differential Privacy

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5. Broader Impact and Future Directions

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References:

• Panda, A., Tang, X., Nasr, M., Choquette-Choo, C. A., & Mittal, P. (2025). Privacy Auditing of Large Language Models. arXiv preprint arXiv:2503.06808. :contentReference[oaicite:44]{index=44}

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