4 papers · 1 filter
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…
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…