UniAud uses synthetic uncorrelated canaries and self-comparison inference to reach near-optimal empirical epsilon lower bounds in one black-box DP audit run, while UniAud++ improves the utility-auditing trade-off via multi-task learning.
Membership inference attacks from first principles,
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UniAud: A Unified Auditing Framework for High Auditing Power and Utility with One Training Run
UniAud uses synthetic uncorrelated canaries and self-comparison inference to reach near-optimal empirical epsilon lower bounds in one black-box DP audit run, while UniAud++ improves the utility-auditing trade-off via multi-task learning.