A new data structure samples any entry of the noise vector in constant time while exactly reproducing the binary tree Gaussian mechanism distribution, applied to DP CountSketches for improved range counting and join size estimation.
Improved differentially private euclidean distance approximation
2 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 2representative citing papers
Determination provenance models tuple supports as elements of a commutative semiring under layered resolutions, inducing a filtration that positive relational algebra respects and that unifies isolation levels with negation semantics.
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A Fast Gaussian Mechanism under Continual Observation, with Applications
A new data structure samples any entry of the noise vector in constant time while exactly reproducing the binary tree Gaussian mechanism distribution, applied to DP CountSketches for improved range counting and join size estimation.
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Determination Provenance: From Ambiguity to Algebra
Determination provenance models tuple supports as elements of a commutative semiring under layered resolutions, inducing a filtration that positive relational algebra respects and that unifies isolation levels with negation semantics.