Privacy Preserving

427 papers with code • 0 benchmarks • 1 datasets

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Use these libraries to find Privacy Preserving models and implementations
3 papers
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Most implemented papers

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

vaseline555/Federated-Learning-in-PyTorch 13 Sep 2019

In this work, we look at the effect such non-identical data distributions has on visual classification via Federated Learning.

Differentially Private Federated Learning: A Client Level Perspective

cyrusgeyer/DiffPrivate_FedLearning ICLR 2019

In such an attack, a client's contribution during training and information about their data set is revealed through analyzing the distributed model.

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.

Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation

CharlieDinh/FEDL 29 Oct 2019

There is an increasing interest in a fast-growing machine learning technique called Federated Learning, in which the model training is distributed over mobile user equipments (UEs), exploiting UEs' local computation and training data.

Personalized Federated Learning with Moreau Envelopes

CharlieDinh/pFedMe NeurIPS 2020

Federated learning (FL) is a decentralized and privacy-preserving machine learning technique in which a group of clients collaborate with a server to learn a global model without sharing clients' data.

Towards Robust and Privacy-preserving Text Representations

lrank/Robust_and_Privacy_preserving_Text_Representations ACL 2018

Written text often provides sufficient clues to identify the author, their gender, age, and other important attributes.

Towards Privacy-Preserving Visual Recognition via Adversarial Training: A Pilot Study

VITA-Group/Privacy-AdversarialLearning ECCV 2018

This paper aims to improve privacy-preserving visual recognition, an increasingly demanded feature in smart camera applications, by formulating a unique adversarial training framework.

Partially Encrypted Machine Learning using Functional Encryption

LaRiffle/collateral-learning 24 May 2019

Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.

Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform

fedlearnAI/fedlearn-algo 8 Jul 2021

We use this platform to demonstrate our research and development results on privacy preserving machine learning algorithms.