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).
Benchmarks
These leaderboards are used to track progress in Privacy Preserving Deep Learning
PA-HMDB51
ConcurrentQA Benchmark
Most implemented papers
Privacy-Preserving Deep Action Recognition: An Adversarial Learning Framework and A New Dataset
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
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We detail a new framework for privacy preserving deep learning and discuss its assets.
Locally Differentially Private (Contextual) Bandits Learning
huang-research-group/LDPbandit2020
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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
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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
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
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
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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
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
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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
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Because learning sometimes involves sensitive data, machine learning algorithms have been extended to offer privacy for training data.
