7 citations · 11 across the 12 of their papers we have counts for
4 papers · 1 filter
Privately Estimating Black-Box Statistics
Günter F. Steinke, Thomas Steinke
Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. Bu…
Correlated Noise Mechanisms for Differentially Private Learning
Krishna Pillutla, Jalaj Upadhyay, Christopher A. Choquette-Choo +9
This monograph explores the design and analysis of correlated noise mechanisms for differential privacy (DP), focusing on their application to private training of AI and machine le…
Hush! Protecting Secrets During Model Training: An Indistinguishability Approach
Arun Ganesh, Brendan McMahan, Milad Nasr +2
We consider the problem of secret protection, in which a business or organization wishes to train a model on their own data, while attempting to not leak secrets potentially contai…
Privately Evaluating Untrusted Black-Box Functions
Ephraim Linder, Sofya Raskhodnikova, Adam Smith +1
We provide tools for sharing sensitive data when the data curator does not know in advance what questions an (untrusted) analyst might ask about the data. The analyst can specify a…