Privacy Preserving
643 papers with code • 0 benchmarks • 1 datasets
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Most implemented papers
Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification
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
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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
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.
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
CharlieDinh/FEDL
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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
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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
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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
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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.
A generic framework for privacy preserving deep learning
SanaAwan5/transfer_learning_on_mnist_spdz
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We detail a new framework for privacy preserving deep learning and discuss its assets.
Partially Encrypted Machine Learning using Functional Encryption
LaRiffle/collateral-learning
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Machine learning on encrypted data has received a lot of attention thanks to recent breakthroughs in homomorphic encryption and secure multi-party computation.
Privacy-preserving Collaborative Learning with Automatic Transformation Search
gaow0007/ATSPrivacy
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CVPR 2021
Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance.
