Research map / Federated learning and privacy / Differential privacy
Inference for Many Quantiles under Local Differential Privacy
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Proposes an online LDP framework for simultaneous inference on multiple quantiles by reducing the problem to categorical one-hot estimation with a projected Robbins-Monro recursion.
Problem
Simultaneous inference for multiple quantiles when data must satisfy local differential privacy.
Approach
Reduces multiple-quantile estimation under LDP to a categorical one-hot estimation problem; a projected Robbins-Monro recursion guarantees noncrossing quantile estimates at every iteration, and Polyak-Ruppert averaging with multivariate self-normalization yields joint inference.
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