arXiv:2606.10481v1 Announce Type: cross Abstract: Parameter-efficient fine-tuning of large language models (LLMs) can exhibit problematic memorization of individual training examples. Empirical privacy auditing (EPA) quantifies this risk by measuring realistic data leakage on membership inference (MI) or reconstruction attacks. A key challenge in EPA is designing ``canary'' examples that are mixed with the privacy-sensitive training data. We propose generating synthetic canaries via high-temperature sampling ($T \geq 0.8$) from LLMs, using prompts tailored to the privacy-sensitive training dat
The increasing deployment of large language models makes their privacy vulnerabilities a critical and immediate concern, driving research into robust auditing methods.
This development improves the ability to quantify and mitigate privacy risks in LLMs, which is essential for their ethical and safe deployment across sensitive applications.
The proposed method for generating synthetic canaries offers a more effective and scalable approach to empirical privacy auditing, potentially leading to more secure LLM training practices.
- · LLM developers
- · Cybersecurity firms
- · Industries handling sensitive data
- · Users concerned with data privacy
- · Malicious actors exploiting data leakage
Improved privacy auditing tools will enable LLMs to be trained with stronger data protection guarantees.
This could accelerate the adoption of LLMs in highly regulated or sensitive sectors by addressing a key trust barrier.
Standardized, auditable privacy practices might emerge as a competitive differentiator or regulatory requirement for AI systems.
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Read at arXiv cs.CL