Detectability without Interference: Efficient Canary Design for Privacy Auditing in One Run
The work addresses a limitation in one-run privacy auditing where sensitive samples (canaries) can interfere with each other, weakening privacy estimates. The proposed IBIS framework optimizes canary selection using influence functions and bilevel optimization to maximize individual detectability while minimizing negative impact on others, significantly improving performance-cost trade-offs.