arXiv:2503.10945v3 Announce Type: replace-cross Abstract: Current practices for reporting differential privacy (DP) guarantees for machine learning (ML) algorithms such as DP-SGD provide an incomplete and potentially misleading picture. For instance, if only a single $(\varepsilon, \delta)$ is known about a mechanism, standard analyses show that there could exist highly accurate inference attacks against training data records, when, upon a more careful analysis, such accurate attacks do not exist for most practical mechanisms. In this position paper, we argue that using _non-asymptotic_ Gaussi
The proliferation of machine learning models and the increasing focus on data privacy necessitate more robust and accurate methods for quantifying privacy guarantees.
This development proposes a more accurate method for evaluating differential privacy, which is critical for trustworthy AI systems and compliance with privacy regulations.
The standard practice for reporting DP guarantees is challenged, potentially leading to a shift towards Gaussian DP for more reliable privacy assessments in ML.
- · Researchers in privacy-preserving ML
- · Organizations prioritizing data privacy
- · Users of AI systems requiring audited privacy
- · Systems relying on incomplete DP metrics
- · Adversaries attempting inference attacks
Improved measurement and reporting of differential privacy in machine learning will become a new standard.
Increased trust in AI systems due to more robust privacy guarantees could accelerate their deployment in sensitive applications.
New regulatory frameworks may emerge, mandating more sophisticated privacy-reporting metrics like Gaussian DP for AI systems.
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Read at arXiv cs.AI