72 citations · 394 across the 35 of their papers we have counts for
43 papers
Counting Distinct Elements Under Person-Level Differential Privacy
Alexander Knop, Thomas Steinke
We study the problem of counting the number of distinct elements in a dataset subject to the constraint of differential privacy. We consider the challenging setting of person-level…
Differentially Private Medians and Interior Points for Non-Pathological Data
Maryam Aliakbarpour, Rose Silver, Thomas Steinke +1
We construct differentially private estimators with low sample complexity that estimate the median of an arbitrary distribution over satisfying very mild moment condit…
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…
Differentially Private Stream Processing at Scale
Bing Zhang, Vadym Doroshenko, Peter Kairouz +6
We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-S…
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…