arXiv:2605.06505v2 Announce Type: replace Abstract: We introduce PACZero, a family of PAC-private zeroth-order mechanisms for fine-tuning large language models that delivers usable utility at $I(S^*; Y_{1:T})=0$. This privacy regime bounds the membership-inference attack (MIA) posterior success rate at the prior, an MIA-resistance level the DP framework matches only at $\varepsilon=0$ and infinite noise. All DP-ZO comparisons below are matched at the MIA posterior level. The key insight is that PAC Privacy charges mutual information only when the release depends on which candidate subset is th
The increasing focus on deploying large language models in sensitive applications necessitates robust privacy guarantees that current Differential Privacy (DP) methods struggle to provide efficiently for fine-tuning.
This breakthrough offers a new, potentially more practical mechanism for achieving strong privacy in AI, which is crucial for commercial adoption and compliance in regulated sectors.
The ability to fine-tune large language models with PAC Privacy at a zero mutual information level introduces a new standard for membership inference attack resistance, potentially enabling broader and safer deployment.
- · AI developers
- · Healthcare sector
- · Financial services
- · Government agencies
- · Malicious actors performing MIA
- · AI models lacking strong privacy
Increased trust and adoption of fine-tuned language models in privacy-sensitive domains.
Reduced regulatory hurdles for AI deployment as privacy guarantees become more robust and quantifiable.
A potential shift in privacy research focus from Differential Privacy to PAC Privacy for language models, influencing future algorithmic design.
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Read at arXiv cs.LG