⚙️ Most privacy auditing focuses on the mechanism.
🖼️ OptiFluence optimizes the canary instead!
Our scalable framework produces canaries with
🔎 near-perfect detection rates (99% TPR@0.1% FPR)
🤸 transferability between architectures
This enables practical regulatory audits!
How does it work?
1. Select a high-influence training sample as the initial canary.
2. Directly optimize the canary's detectability over the entire training procedure (memory-efficient!)
OptiFluence is thus independent of architecture or mechanism.
Check out the paper for more details and ablations!
Joint work with @AerniMichael, Junrui (Arielle) Zhang, @NicolasPapernot, @florian_tramer
See you today at 2:30PM at our poster at #ICML 2026! (Hall A)
🌐 Website: cleverhans.io/optifluence/
💻 Code: github.com/cleverhans-lab/op…
Can ML models be trained under fairness constraints with formal DP guarantees, without sacrificing utility?
New paper at ICLR 2026: RaCO-DP
Poster: Pavilion 4 (later today!)
🧵
(w/ @tudorcebere, Michael Menart, Aurélien Bellet, @NicolasPapernot )
Unlike regularization-based methods, RaCO-DP enforces bounds directly by construction: no hyperparameter tuning, no extra privacy cost.
Also scales to many simultaneous constraints without degradation, and runs three orders of magnitude faster than the prior SOTA, DP-FERMI.
The privacy-fairness tension is considerably weaker than previously believed. It appears largely to be an artifact of algorithm design.
🌐 cleverhans.io/dp-raco
📄 iclr.cc/virtual/2026/poster/…
💻 github.com/cleverhans-lab/dp…
Mohammad Yaghini retweeted
Curious about novel risks that AI introduces for patients and healthcare providers?
We're hosting an #AIUK Fringe event to explore Privacy and Fairness in AI for Health – Register now!
private-fair-ai.github.io/
Date: 27 March, 10:00-16:30 GMT
Location: @turinginst, London
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