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58 search results found

Secretflow ⭐ 2,101

A unified framework for privacy-preserving data analysis and machine learning

Pfllib ⭐ 1,147

We expose this user-friendly algorithm library (with an integrated evaluation platform) for beginners who intend to start federated learning (FL) study

Differential Privacy Library ⭐ 753

Diffprivlib: The IBM Differential Privacy Library

Gretel Synthetics ⭐ 490

Synthetic data generators for structured and unstructured text, featuring differentially private learning.

The Python Differential Privacy Library. Built on top of: https://github.com/google/differential-privacy

Differential Privacy Based Federated Learning ⭐ 212

Everything you want about DP-Based Federated Learning, including Papers and Code. (Mechanism: Laplace or Gaussian, Dataset: femnist, shakespeare, mnist, cifar-10 and fashion-mnist. )

Privacyengcollabspace ⭐ 204

Privacy Engineering Collaboration Space

Cross-silo Federated Learning playground in Python. Discover 7 real-world federated datasets to test your new FL strategies and try to beat the leaderboard.

Awesome Differential Privacy And Meachine Learning ⭐ 163

机器学习和差分隐私的论文笔记和代码仓

Privacy Preserving Collaborative Encrypted Network Traffic Classification (Differential Privacy, Federated Learning, Membership Inference Attack, Encrypted Traffic Classification)

Awesome Differential Privacy ⭐ 77

Differential private machine learning

Smartnoise Core Python ⭐ 76

Python language bindings for smartnoise-core.

Ml Doctor ⭐ 74

Code for ML Doctor

👁🙅‍♀️Differentially Private Optimization Algorithms for PyTorch

Private Transformers ⭐ 69

make differentially private training of transformers easy

Netshare ⭐ 59

(SIGCOMM '22) Practical GAN-based Synthetic IP Header Trace Generation using NetShare

Pure Ldp ⭐ 57

Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.

Rajappan ⭐ 44

An All in one Project for Digital Privacy. A step towards a PRIVATE FUTURE

K Anonymity ⭐ 43

Evaluating variety of k-Anonymity techniques.

Privacyfl ⭐ 39

A Simulator for Privacy Preserving Federated Learning

Differential Privacy ⭐ 37

Naive implementation of basic Differential-Privacy framework and algorithms

Private Data Generation ⭐ 32

A toolbox for differentially private data generation

[ICLR 2024] Generating DP Synthetic Data without Training

SDNist: Benchmark data and evaluation tools for data synthesizers.

Multi Freq Ldpy ⭐ 24

Multiple Frequency Estimation Under Local Differential Privacy in Python

Dp_cgans ⭐ 24

A library to generate synthetic tabular or RDF data using Conditional Generative Adversary Networks (GANs) combined with Differential Privacy techniques.

Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/1610.05755)

Code for CIKM 2021 paper: Differentially Private Federated Knowledge Graphs Embedding (https://arxiv.org/abs/2105.07615)

Differential Privacy Bayesian Optimization ⭐ 22

This repo contains the underlying code for all the experiments from the paper: "Automatic Discovery of Privacy-Utility Pareto Fronts"

dpart: General, flexible, and scalable framework for differentially private synthetic data generation, developed by hazy.

Anonymisation ⭐ 20

Repertoire sur l'anonymisation

Federated ⭐ 18

Bachelor's Thesis in Computer Science: Privacy-Preserving Federated Learning Applied to Decentralized Data

Greynsights ⭐ 18

Privacy-Preserving Data Analysis using Pandas

Private Ml For Health ⭐ 18

Dopamine: Differentially Private Federated Learning on Medical Data (AAAI - PPAI)

Private Cross Silo Fl ⭐ 17

[NeurIPS 2022] JAX/Haiku implementation of "On Privacy and Personalization in Cross-Silo Federated Learning"

Dpcreator ⭐ 16

Web application to demonstrate differential privacy using the OpenDP Core library

Datalens ⭐ 13

[CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long*, Luka Rimanic, Ce Zhang, Bo Li

Dp Sniper ⭐ 12

A machine-learning-based tool for discovering differential privacy violations in black-box algorithms.

Apple Differential Privacy ⭐ 11

Apple Differential Privacy Implementation

Pytorch Privacy ⭐ 11

simple Differential Privacy in PyTorch

Collection of tools and resources for managing the statistical disclosure control of trained machine learning models

Amun is a framework that achieves privacy-preserving process mining using differential privacy.

Reconstruction Attack on Differential Private Trajectory Protection Mechanisms

Source code for LDPTrace: Locally Differentially Private Trajectory Synthesis. VLDB 2023.

Repository for the EDBT'23 paper "Frequency Estimation of Evolving Data Under Local Differential Privacy".

Data Privacy Uga ⭐ 8

Data Privacy - CSCI 6900, Fall '16

Spark Privacy Preserver ⭐ 7

Anonymizing Library for Apache Spark

Official JAX/Haiku implementation of the paper "Differentially Private Adaptive Optimization with Delayed Preconditioners"

Federated_he ⭐ 7

Federated learning with homomorphic encryption enables multiple parties to securely co-train artificial intelligence models in pathology and radiology, reaching state-of-the-art performance with privacy guarantees.

DPLab: Benchmarking Differential Privacy Aggregation Operations

Differential privacy application of CNN in Computer Vision

Private_vision ⭐ 6

Differential Privacy ⭐ 5

Differential Privacy Algorithms in JS

A light-weight imperative language for developing provably privacy-preserving algorithms

Linkteller ⭐ 5

LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis

Secure Federated Learning ⭐ 5

Secure Federated Learning Framework with Encryption Aggregation and Integer Encoding Method.

Nist_differential_privacy_synthetic_data_challenge ⭐ 5

UCLANesl - NIST Differential Privacy Challenge (Match 3)

Dpsgd Optimizer ⭐ 5

Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enforces privacy by clipping and sanitising the gradients with Gaussian noise during training.

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