6 papers
Propose-Filter-Release: Scaling Noise to Typical Sensitivity via Private Filtering
Moshe Shenfeld, Eliad Tsfadia, Katrina Ligett
An estimator can be accurate and stable on typical data, yet sensitive to a few possible records. Differentially private mechanisms commonly scale their noise to the worst-case inf…
The Fast Mixing Mechanism for Differential Privacy
Omri Lev, Moshe Shenfeld, Vishwak Srinivasan +2
Randomized sketching is a central tool for compressing large-scale optimization problems while preserving accuracy. In particular, sketches that are based on structured matrices, s…
Near-Optimal Private Linear Regression via Iterative Hessian Mixing
Omri Lev, Moshe Shenfeld, Vishwak Srinivasan +2
We study differentially private ordinary least squares (DP-OLS) with bounded data via sketching-based mechanisms. While Gaussian sketching approaches have been explored for…
Differentially Private Nonparametric Confidence Intervals Under Minimal Distributional Assumptions
Tomer Shoham, Moshe Shenfeld, Noa Velner-Harris +1
We consider the problem of constructing differentially private nonparametric confidence intervals (CIs) for an arbitrary quantity using resampling. A growing body of work has adapt…
The Gaussian Mixing Mechanism: Renyi Differential Privacy via Gaussian Sketches
Omri Lev, Vishwak Srinivasan, Moshe Shenfeld +3
Gaussian sketching, which consists of pre-multiplying the data with a random Gaussian matrix, is a widely used technique for multiple problems in data science and machine learning,…
How Well Can Differential Privacy Be Audited in One Run?
Amit Keinan, Moshe Shenfeld, Katrina Ligett
Recent methods for auditing the privacy of machine learning algorithms have improved computational efficiency by simultaneously intervening on multiple training examples in a singl…