arXiv:2606.12733v2 Announce Type: replace Abstract: Privacy auditing provides an important safeguard by estimating the actual information leaked by a model, thus ensuring that theoretical privacy guarantees hold in practice. We study empirical privacy auditing for differentially private (DP) machine learning, focusing on efficient one-run methods for mechanisms such as DP-SGD. Prior one-run approaches threshold training examples or "canaries" into binary membership guesses, which discards useful information. We show that, in the white-box DP-SGD setting, canary-aligned signals naturally form a
The increasing deployment of differential privacy in AI models, particularly for sensitive data, necessitates robust methods for verifying privacy guarantees in practice.
This research offers a more efficient and accurate way to audit privacy in AI systems, addressing a critical concern as AI adoption expands into regulated and high-stakes domains.
The ability to perform more effective one-run privacy auditing of DP-SGD models can improve trust in differentially private AI and enhance regulatory compliance.
- · AI developers
- · Privacy researchers
- · Organizations handling sensitive data
- · Users of AI with privacy concerns
- · Attackers attempting to extract private information
Improved private AI models can be more reliably deployed in fields like healthcare and finance due to stronger empirical privacy guarantees.
This could lead to a broader adoption of differential privacy as its real-world effectiveness becomes more verifiable and auditable.
Enhanced trust in AI privacy might accelerate the development of personalized AI services that operate on highly sensitive user data.
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Read at arXiv cs.LG