Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be
Secretly Coded into the Classifiers' Outputs

Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs

25 May 2021

Mohammad Malekzadeh

Anastasia Borovykh

Deniz Gündüz

Papers citing "Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs"

4 / 4 papers shown

Title
Salted Inference: Enhancing Privacy while Maintaining Efficiency of Split Inference in Mobile Computing Mohammad Malekzadeh F. Kawsar FedML 27 5 0 20 Oct 2023
Training privacy-preserving video analytics pipelines by suppressing features that reveal information about private attributes C. Li Andrea Cavallaro PICV 14 0 0 05 Mar 2022
Dopamine: Differentially Private Federated Learning on Medical Data Mohammad Malekzadeh Burak Hasircioglu N. Mital K. Katarya M. E. Ozfatura Deniz Gündüz OOD FedML 26 51 0 27 Jan 2021
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn Pieter Abbeel Sergey Levine OOD 329 11,681 0 09 Mar 2017