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Search results for jupyter notebook differential privacy
differential-privacy
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jupyter-notebook
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17 search results found
Opacus
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1,535
Training PyTorch models with differential privacy
Smartnoise Samples
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103
Code samples and documentation for SmartNoise differential privacy tools
Sprint_gan
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79
Privacy-preserving generative deep neural networks support clinical data sharing
Lpgnn
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39
Locally Private Graph Neural Networks (ACM CCS 2021)
Gap
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23
GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation (USENIX Security '23)
Recsys2021 Pursuing Privacy
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18
Material supporting the tutorial "Pursuing Privacy in Recommender Systems: The View of Users and Researchers from Regulations to Applications" held at the 15th ACM Conference on Recommender Systems in Amsterdam, Netherlands
Privacy Preserving Bandits
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14
Privacy-Preserving Bandits (MLSys'20)
Srijan Gsoc 2020
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12
Healthcare-Researcher-Connector Package: Federated Learning tool for bridging the gap between Healthcare providers and researchers
Sagemaker Privacy For Nlp
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12
A solution that helps apply a privacy preserving mechanism to NLP data, using Amazon SageMaker.
Ldp Protocols Mobility Cdrs
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11
Implementation of local differential privacy in Python language.
Apple Differential Privacy
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11
Apple Differential Privacy Implementation
Pets Bootcamp
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11
A collection of demos and utilities prepared ahead of the Vector Institute Privacy Enhancing Techniques (PETs) Bootcamp.
Bsc Thesis
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10
🎓🔒 Creating, Analyzing and Testing Differential Privacy Protocols, aiming in Data Protection and Anonymization.
Ldp Fairness Impact
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7
Repository for the DBSec 2023 paper "(Local) Differential Privacy has NO Disparate Impact on Fairness"
Thresholdout Explorations
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6
playing with Dwork's adaptive holdout and how to use it for a grid-search
Privacy_enhancing_technologies_portfolio
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5
This repository contains projects applying privacy-enhancing technologies.
Dp Carbon Footprint
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5
Analysing the carbon footprint of Differential privacy guarantees on Vanilla BERT ( modelled for news classification ), MNIST and the Cartpole Problem using Opacus. [ Accepted to appear at ICML 2021 - Workshop on Socially Responsible Machine Learning ]
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