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An Optimal and Scalable Matrix Mechanism for Noisy Marginals under Convex Loss Functions. (arXiv:2305.08175v1 [cs.DB])
cs.CR updates on arXiv.org arxiv.org
Noisy marginals are a common form of confidentiality-protecting data release
and are useful for many downstream tasks such as contingency table analysis,
construction of Bayesian networks, and even synthetic data generation. Privacy
mechanisms that provide unbiased noisy answers to linear queries (such as
marginals) are known as matrix mechanisms.
We propose ResidualPlanner, a matrix mechanism for marginals with Gaussian
noise that is both optimal and scalable. ResidualPlanner can optimize for many
loss functions that can be written as a convex …
analysis confidentiality construction data functions loss matrix networks privacy protecting release synthetic synthetic data under