16 citations · 47 across the 10 of their papers we have counts for
5 papers · 1 filter
Private Geometric Median
Mahdi Haghifam, Thomas Steinke, Jonathan Ullman
In this paper, we study differentially private (DP) algorithms for computing the geometric median (GM) of a dataset: Given points, in , the goal i…
Faster Differentially Private Convex Optimization via Second-Order Methods
Arun Ganesh, Mahdi Haghifam, Thomas Steinke +1
Differentially private (stochastic) gradient descent is the workhorse of DP private machine learning in both the convex and non-convex settings. Without privacy constraints, second…
Privacy Auditing with One (1) Training Run
Thomas Steinke, Milad Nasr, Matthew Jagielski
We propose a scheme for auditing differentially private machine learning systems with a single training run. This exploits the parallelism of being able to add or remove multiple t…
Why Is Public Pretraining Necessary for Private Model Training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr +5
In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported with the use of pretraining on publicly…
Tight Auditing of Differentially Private Machine Learning
Milad Nasr, Jamie Hayes, Thomas Steinke +5
Auditing mechanisms for differential privacy use probabilistic means to empirically estimate the privacy level of an algorithm. For private machine learning, existing auditing mech…