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Subspace Differential Privacy

AAAI 2022 · Federated learning and privacy › Differential privacy · approach: Privacy accounting and mechanisms · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use projected Laplace noise while enforcing state population totalsCounty-level 2020 Census population counts · 2020 Census demonstration dataerror biasHelped
Enforce linear equality invariants by projecting Gaussian noise into the invariant null spaceHourly group-by-building counts · Tsinghua campus mobility dataerror scaleMixed
Use extension Gaussian noise calibrated to the null-space query sensitivity instead of projected Gaussian noise calibrated to the full queryInduced subspace Gaussian mechanism · General queriesmean squared errorHelped
Use projected Laplace noise with the state population invariantCounty-level Census counts · 2020 Census demonstration datanegative error association with true county population: 37 out of 48; 11 statistically significant at α = 0.01Helped

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