做隐私审计或差分隐私研究的团队，这篇论文直接解决了单次运行审计中金丝雀干扰的痛点，提出的方法计算效率高且效果更好，值得点开看具体实现。
该论文研究了机器学习模型隐私审计中的金丝雀（canary）生成问题，旨在通过单次训练运行高效评估隐私泄露。作者提出一种结合影响函数贪婪初始化与双层优化的方法，生成既高可检测又低干扰的金丝雀，通过促进嵌入空间多样性减少金丝雀间干扰。实验表明，该方法在更低计算成本下获得比现有方法更强的隐私泄露估计，为差分隐私审计提供了实用改进。
Detectability in Diversity: Improved Canary Crafting for Privacy Auditing in One Run
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 that interference between canaries contributes to weaker leakage estimates compared to multi-run methods, we propose to optimize canaries to be both highly detectable and minimally interfering. Our approach combines a greedy initialization based on influence functions with a bilevel optimization procedure that maximizes distinguishability while promoting diversity in embedding space, enabling the use of computationally efficient bilevel algorithms. Experiments show that our method achieves stronger privacy leakage estimates at a lower computational cost than existing canary crafting approaches.