arXiv:2605.27292v1 Announce Type: new Abstract: Privacy auditing aims to empirically assess privacy leakage in machine learning models using membership inference attacks (MIAs), and to derive lower bounds on differential privacy (DP) parameters. Recent one-run auditing methods address the high cost of standard approaches by relying on a single training run with multiple "canary" points whose inclusion or exclusion must be detected by the auditor. In this work, we study the problem of efficiently crafting canaries for one-run privacy auditing. Motivated by recent theoretical insights suggesting
The increasing deployment of machine learning models across sensitive domains necessitates robust privacy auditing methods, pushing research into more efficient techniques like one-run auditing.
This development improves the practical application of privacy auditing, enabling better assessment of privacy leakage and differential privacy parameters in AI models, which is crucial for regulatory compliance and public trust.
The efficiency of privacy auditing is significantly enhanced, allowing for more frequent and less resource-intensive assessments of AI model privacy guarantees.
- · AI developers
- · Privacy researchers
- · Data privacy regulators
- · End-users of AI services
- · Malicious actors exploiting data leakage
More accurate and faster privacy assessments for machine learning models will become standard practice.
Increased adoption of privacy-preserving AI techniques due to easier auditing will foster greater trust in AI systems.
New regulatory frameworks may emerge, leveraging these improved auditing capabilities to enforce stricter privacy standards on AI deployments.
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Read at arXiv cs.LG