Pushing the Boundaries of Private, Large-Scale Query Answering. (arXiv:2302.04833v1 [cs.CR])

We address the problem of efficiently and effectively answering large numbers
of queries on a sensitive dataset while ensuring differential privacy (DP). We
separately analyze this problem in two distinct settings, grounding our work in
a state-of-the-art DP mechanism for large-scale query answering: the Relaxed
Adaptive Projection (RAP) mechanism.

The first setting is a classic setting in DP literature where all queries are
known to the mechanism in advance. Within this setting, we identify challenges
in the RAP mechanism's original …

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