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Testing the Lipschitz property over product distributions with applications to data privacy
In the past few years, the focus of research in the area of statistical data privacy has been in
designing algorithms for various problems which satisfy some rigorous notions of privacy …
designing algorithms for various problems which satisfy some rigorous notions of privacy …
Locally computing edge orientations
We consider the question of orienting the edges in a graph $ G $ such that every vertex has
bounded out-degree. For graphs of arboricity $\alpha $, there is an orientation in which …
bounded out-degree. For graphs of arboricity $\alpha $, there is an orientation in which …
Can We Locally Compute Sparse Connected Subgraphs?
R Rubinfeld - International Computer Science Symposium in Russia, 2017 - Springer
How can we solve optimization problems on data that is so large, that we cannot hope to
view more than a miniscule fraction of it? When attempting to solve optimization problems on …
view more than a miniscule fraction of it? When attempting to solve optimization problems on …
Limitations of local filters of Lipschitz and monotone functions
We study local filters for two properties of functions of the form f:{0, 1} d→ R: the Lipschitz
property and monotonicity. A local filter with additive error a is a randomized algorithm that is …
property and monotonicity. A local filter with additive error a is a randomized algorithm that is …
Testing lipschitz property over product distribution and its applications to statistical data privacy
In this work, we present a connection between Lipschitz property testing and a relaxed
notion of differential privacy, where we assume that the datasets are being sampled from a …
notion of differential privacy, where we assume that the datasets are being sampled from a …