Privacy Preserving
506 papers with code • 0 benchmarks • 1 datasets
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Libraries
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Most implemented papers
Clustered Federated Learning: Model-Agnostic Distributed Multi-Task Optimization under Privacy Constraints
Federated Learning (FL) is currently the most widely adopted framework for collaborative training of (deep) machine learning models under privacy constraints.
Privacy-Preserving Gradient Boosting Decision Trees
Specifically, by investigating the property of gradient and the contribution of each tree in GBDTs, we propose to adaptively control the gradients of training data for each iteration and leaf node clipping in order to tighten the sensitivity bounds.
Privacy-preserving data sharing via probabilistic modelling
DPBayes/data-sharing-examples
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Differential privacy allows quantifying privacy loss resulting from accessing sensitive personal data.
Robust Aggregation for Federated Learning
krishnap25/RFA
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arXiv preprint 2019
We present a robust aggregation approach to make federated learning robust to settings when a fraction of the devices may be sending corrupted updates to the server.
Privacy-Preserving News Recommendation Model Learning
Extensive experiments on a real-world dataset show the effectiveness of our method in news recommendation model training with privacy protection.
COVID-19 and Your Smartphone: BLE-based Smart Contact Tracing
SCT's contribution is two-fold: a) classification of the user's contact as high/low-risk using precise proximity sensing, and b) user anonymity using a privacy-preserving communications protocol.
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.
P3GM: Private High-Dimensional Data Release via Privacy Preserving Phased Generative Model
tsubasat/P3GM
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The state-of-the-art approach for this problem is to build a generative model under differential privacy, which offers a rigorous privacy guarantee.
Adversarial Privacy-preserving Filter
adversarial-for-goodness/APF
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While widely adopted in practical applications, face recognition has been critically discussed regarding the malicious use of face images and the potential privacy problems, e. g., deceiving payment system and causing personal sabotage.
Performance Optimization for Federated Person Re-identification via Benchmark Analysis
EasyFL-AI/EasyFL
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Then we propose two optimization methods: (1) To address the unbalanced weight problem, we propose a new method to dynamically change the weights according to the scale of model changes in clients in each training round; (2) To facilitate convergence, we adopt knowledge distillation to refine the server model with knowledge generated from client models on a public dataset.
