34 citations · 111 across the 18 of their papers we have counts for
41 papers
Private Multi-Winner Voting for Machine Learning
Adam Dziedzic, Christopher A Choquette-Choo, Natalie Dullerud +6
Private multi-winner voting is the task of revealing -hot binary vectors satisfying a bounded differential privacy (DP) guarantee. This task has been understudied in machine lea…
Federated Boosted Decision Trees with Differential Privacy
Samuel Maddock, Graham Cormode, Tianhao Wang +2
There is great demand for scalable, secure, and efficient privacy-preserving machine learning models that can be trained over distributed data. While deep learning models typically…
Optimal Membership Inference Bounds for Adaptive Composition of Sampled Gaussian Mechanisms
Saeed Mahloujifar, Alexandre Sablayrolles, Graham Cormode +1
Given a trained model and a data sample, membership-inference (MI) attacks predict whether the sample was in the model's training set. A common countermeasure against MI attacks is…
Using Illustrations to Communicate Differential Privacy Trust Models: An Investigation of Users' Comprehension, Perception, and Data Sharing Decision
Aiping Xiong, Chuhao Wu, Tianhao Wang +4
Proper communication is key to the adoption and implementation of differential privacy (DP). However, a prior study found that laypeople did not understand the data perturbation pr…
An Exploration of Multicalibration Uniform Convergence Bounds
Harrison Rosenberg, Robi Bhattacharjee, Kassem Fawaz +1
Recent works have investigated the sample complexity necessary for fair machine learning. The most advanced of such sample complexity bounds are developed by analyzing multicalibra…
Lightweight, Multi-Stage, Compiler-Assisted Application Specialization
Mohannad Alhanahnah, Rithik Jain, Vaibhav Rastogi +2
Program debloating aims to enhance the performance and reduce the attack surface of bloated applications. Several techniques have been recently proposed to specialize programs. The…