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Found 9 papers, 0 papers with code
Gaussian Noise is Nearly Instance Optimal for Private Unbiased Mean Estimation
We investigate unbiased high-dimensional mean estimators in differential privacy.
Learning versus Refutation in Noninteractive Local Differential Privacy
We study two basic statistical tasks in non-interactive local differential privacy (LDP): learning and refutation.
Private Query Release via the Johnson-Lindenstrauss Transform
We introduce a new method for releasing answers to statistical queries with differential privacy, based on the Johnson-Lindenstrauss lemma.
Private Query Release Assisted by Public Data
In comparison, with only private samples, this problem cannot be solved even for simple query classes with VC-dimension one, and without any private samples, a larger public sample of size $d/\alpha^2$ is needed.
Maximizing Determinants under Matroid Constraints
Our main result is a new approximation algorithm with an approximation guarantee that depends only on the dimension $d$ of the vectors and not on the size $k$ of the output set.
Locally Private Hypothesis Selection
Absent privacy constraints, this problem requires $O(\log k)$ samples from $p$, and it was recently shown that the same complexity is achievable under (central) differential privacy.
The Power of Factorization Mechanisms in Local and Central Differential Privacy
We give new characterizations of the sample complexity of answering linear queries (statistical queries) in the local and central models of differential privacy: *In the non-interactive local model, we give the first approximate characterization of the sample complexity.
Proportional Volume Sampling and Approximation Algorithms for A-Optimal Design
Our main result is to obtain improved approximation algorithms for the $A$-optimal design problem by introducing the proportional volume sampling algorithm.
Approximate Near Neighbors for General Symmetric Norms
We show that every symmetric normed space admits an efficient nearest neighbor search data structure with doubly-logarithmic approximation.
