22 citations · 85 across the 11 of their papers we have counts for
8 papers · 1 filter
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…
Composition of Differential Privacy & Privacy Amplification by Subsampling
Thomas Steinke
This chapter is meant to be part of the book "Differential Privacy for Artificial Intelligence Applications." We give an introduction to the most important property of differential…
Algorithms with More Granular Differential Privacy Guarantees
Badih Ghazi, Ravi Kumar, Pasin Manurangsi +1
Differential privacy is often applied with a privacy parameter that is larger than the theory suggests is ideal; various informal justifications for tolerating large privacy parame…
The Permute-and-Flip Mechanism is Identical to Report-Noisy-Max with Exponential Noise
Zeyu Ding, Daniel Kifer, Sayed M. Saghaian N. E. +4
The permute-and-flip mechanism is a recently proposed differentially private selection algorithm that was shown to outperform the exponential mechanism. In this paper, we show that…
Privately Learning Subspaces
Vikrant Singhal, Thomas Steinke
Private data analysis suffers a costly curse of dimensionality. However, the data often has an underlying low-dimensional structure. For example, when optimizing via gradient desce…
Multi-Central Differential Privacy
Thomas Steinke
Differential privacy is typically studied in the central model where a trusted "aggregator" holds the sensitive data of all the individuals and is responsible for protecting their…