Privacy Preserving Deep Learning

26 papers with code • 0 benchmarks • 3 datasets

The goal of privacy-preserving (deep) learning is to train a model while preserving privacy of the training dataset. Typically, it is understood that the trained model should be privacy-preserving (e.g., due to the training algorithm being differentially private).

Most implemented papers

Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset

VITA-Group/PA-HMDB51 12 Jun 2019

We first discuss an innovative heuristic of cross-dataset training and evaluation, enabling the use of multiple single-task datasets (one with target task labels and the other with privacy labels) in our problem.

A generic framework for privacy preserving deep learning

OpenMined/PySyft 9 Nov 2018

We detail a new framework for privacy preserving deep learning and discuss its assets.

Locally Differentially Private (Contextual) Bandits Learning

huang-research-group/LDPbandit2020 NeurIPS 2020

We study locally differentially private (LDP) bandits learning in this paper.

ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing

LaRiffle/AriaNN 8 Jun 2020

We evaluate our end-to-end system for private inference between distant servers on standard neural networks such as AlexNet, VGG16 or ResNet18, and for private training on smaller networks like LeNet.

Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning

kvgarimella/sisyphus-ppml 26 Jul 2021

In this work, we ask: Is it feasible to substitute all ReLUs with low-degree polynomial activation functions for building deep, privacy-friendly neural networks?

Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging

TUM-AIMED/2.5DAttention 3 Feb 2023

In this work, we evaluated the effect of privacy-preserving training of AI models regarding accuracy and fairness compared to non-private training.

Towards Fair and Privacy-Preserving Federated Deep Models

lingjuanlv/FPPDL 4 Jun 2019

This problem can be addressed by either a centralized framework that deploys a central server to train a global model on the joint data from all parties, or a distributed framework that leverages a parameter server to aggregate local model updates.

Fawkes: Protecting Privacy against Unauthorized Deep Learning Models

Shawn-Shan/fawkes 19 Feb 2020

In this paper, we propose Fawkes, a system that helps individuals inoculate their images against unauthorized facial recognition models.

Locally Private Graph Neural Networks

sisaman/lpgnn 9 Jun 2020

In this paper, we study the problem of node data privacy, where graph nodes have potentially sensitive data that is kept private, but they could be beneficial for a central server for training a GNN over the graph.

Tempered Sigmoid Activations for Deep Learning with Differential Privacy

woodyx218/opacus_global_clipping 28 Jul 2020

Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.