Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees

21 February 2022

Papers citing "Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees"

6 / 6 papers shown

Title
DP-GPL: Differentially Private Graph Prompt Learning Jing Xu Franziska Boenisch Iyiola Emmanuel Olatunji Adam Dziedzic AAML 55 0 0 13 Mar 2025
Controlled privacy leakage propagation throughout overlapping grouped learning Shahrzad Kiani Franziska Boenisch S. Draper FedML 72 0 0 06 Mar 2025
Guarding the Privacy of Label-Only Access to Neural Network Classifiers via iDP Verification Anan Kabaha Dana Drachsler-Cohen AAML 48 0 0 23 Feb 2025
Cross-silo Federated Learning with Record-level Personalized Differential Privacy Junxu Liu Jian Lou Li Xiong Jinfei Liu Xiaofeng Meng 43 6 0 29 Jan 2024
CaPC Learning: Confidential and Private Collaborative Learning Christopher A. Choquette-Choo Natalie Dullerud Adam Dziedzic Yunxiang Zhang S. Jha Nicolas Papernot Xiao Wang FedML 73 57 0 09 Feb 2021
Individual Privacy Accounting via a Renyi Filter Vitaly Feldman Tijana Zrnic 61 86 0 25 Aug 2020