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Systems with near-perfect EER can still expose significant privacy vulnerabilities, with worst-case disclosures effectively doubling an attacker's success rate.
Standard shuffling in federated learning doesn't stop source inference attacks, but parameter-level shuffling with residue number systems does, without hurting accuracy.
