The Sample Complexity of Membership Inference and Privacy Auditing
The study investigates membership inference attacks against Gaussian mean estimation in simple settings. It proves that any successful attack requires a sample complexity of at least $\omega(n)$ samples, which is significantly more than the $n$ samples used for training. This implies that existing methods might underestimate privacy risks and that better attacks are possible when distribution knowledge is available.