7 papers
Efficient privacy loss accounting for subsampling and random allocation
Vitaly Feldman, Moshe Shenfeld
We consider the privacy amplification properties of a sampling scheme in which a user's data isused in steps chosen randomly and uniformly from a sequence (or set) of steps…
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…
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…
Privacy amplification by random allocation
Vitaly Feldman, Moshe Shenfeld
We consider the privacy amplification properties of a sampling scheme in which a user's data is used in k steps chosen randomly and uniformly from a sequence (or set) of t steps. T…