Privacy Preserving Deep Learning

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).

Papers

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Showing 26–50 of 59 papers

Title	Date	Tasks	Status	Hype
Backpropagation Clipping for Deep Learning with Differential Privacy	Feb 10, 2022	Deep LearningPrivacy Preserving	CodeCode Available	0
DP-FP: Differentially Private Forward Propagation for Large Models	Dec 29, 2021	Privacy PreservingPrivacy Preserving Deep Learning	—Unverified	0
SoK: Privacy-preserving Deep Learning with Homomorphic Encryption	Dec 23, 2021	Deep LearningPrivacy Preserving	—Unverified	0
Homogeneous Learning: Self-Attention Decentralized Deep Learning	Oct 11, 2021	Deep LearningFederated Learning	CodeCode Available	0
Towards Secure and Practical Machine Learning via Secret Sharing and Random Permutation	Aug 17, 2021	BIG-bench Machine LearningPrivacy Preserving	CodeCode Available	0
Sisyphus: A Cautionary Tale of Using Low-Degree Polynomial Activations in Privacy-Preserving Deep Learning	Jul 26, 2021	Privacy PreservingPrivacy Preserving Deep Learning	CodeCode Available	0
Towards a Privacy-preserving Deep Learning-based Network Intrusion Detection in Data Distribution Services	Jun 12, 2021	Deep LearningIntrusion Detection	—Unverified	0
Antipodes of Label Differential Privacy: PATE and ALIBI	Jun 7, 2021	Bayesian InferenceMemorization	CodeCode Available	1
Variational Leakage: The Role of Information Complexity in Privacy Leakage	Jun 5, 2021	AttributeFace Recognition	CodeCode Available	0
CryptGPU: Fast Privacy-Preserving Machine Learning on the GPU	Apr 22, 2021	BIG-bench Machine LearningCPU	CodeCode Available	1
Practical Privacy Filters and Odometers with Rényi Differential Privacy and Applications to Differentially Private Deep Learning	Mar 2, 2021	Privacy PreservingPrivacy Preserving Deep Learning	CodeCode Available	0
Oriole: Thwarting Privacy against Trustworthy Deep Learning Models	Feb 23, 2021	Data PoisoningDeep Learning	—Unverified	0
Can we Generalize and Distribute Private Representation Learning?	Oct 5, 2020	Federated LearningGenerative Adversarial Network	CodeCode Available	0
Secure Data Sharing With Flow Model	Sep 24, 2020	BIG-bench Machine LearningImage Classification	CodeCode Available	0
GuardNN: Secure Accelerator Architecture for Privacy-Preserving Deep Learning	Aug 26, 2020	Deep LearningPrivacy Preserving	—Unverified	0
Tempered Sigmoid Activations for Deep Learning with Differential Privacy	Jul 28, 2020	Deep LearningPrivacy Preserving	CodeCode Available	1
How to Democratise and Protect AI: Fair and Differentially Private Decentralised Deep Learning	Jul 18, 2020	Deep LearningFairness	—Unverified	0
MPC Protocol for G-module and its Application in Secure Compare and ReLU	Jul 8, 2020	Privacy PreservingPrivacy Preserving Deep Learning	—Unverified	0
Security and Privacy Preserving Deep Learning	Jun 23, 2020	BIG-bench Machine LearningDeep Learning	—Unverified	0
Rethinking Privacy Preserving Deep Learning: How to Evaluate and Thwart Privacy Attacks	Jun 20, 2020	Privacy PreservingPrivacy Preserving Deep Learning	—Unverified	0
Locally Private Graph Neural Networks	Jun 9, 2020	Federated LearningNode Classification	CodeCode Available	1
ARIANN: Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing	Jun 8, 2020	CPUFederated Learning	CodeCode Available	1
Locally Differentially Private (Contextual) Bandits Learning	Jun 1, 2020	Multi-Armed BanditsPrivacy Preserving Deep Learning	CodeCode Available	0
Fawkes: Protecting Privacy against Unauthorized Deep Learning Models	Feb 19, 2020	Deep LearningFace Recognition	CodeCode Available	3
Privacy-Preserving Deep Learning Computation for Geo-Distributed Medical Big-Data Platforms	Jan 9, 2020	Deep LearningPrivacy Preserving	—Unverified	0

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