Shuangqing-Xu

Shuangqing-Xu's Stars

• dpsa4fl/overview

  Differential Privacy for Federated Learning with Secure Aggregation

  4

• AshleyHan/SmileGNN

  Language:Python141

• dayu11/Differentially-Private-Deep-Learning

  This repo implements several algorithms for learning with differential privacy.

  Language:Python10120

• thecml/dpsgd-optimizer

  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.

  Language:Python416

• ammartahir24/SecureAggregation

  An implementation of Secure Aggregation algorithm based on "Practical Secure Aggregation for Privacy-Preserving Machine Learning (Bonawitz et. al)" in Python.

  Language:Python8120

• KVSAGG/KVSAGG

  Language:Python33

• wangsw/PrivacyAmplification

  Unified, Simplified, Tight and Fast Privacy Amplification in the Shuffle Model of Differential Privacy

  Language:Python10

• wuwuz/sparse-vector-aggregation

  Language:C++61

• forestneo/sunPytools

  Useful tools for differential privacy

  Language:Python21453

• graykode/gpt-2-Pytorch

  Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

  Language:Python971227

• Jaap-Meerhof/Federated_XGBoost_Python

  FederBoost's Federated Gradient Boosting Decision Tree Algorithm, Federated enabled Membership Inference

  Language:Python121

• DPrio-PoPETs/dprio

  Language:Rust41

• Antonious-Grigis/Shuffled-Model-of-Differential-Privacy-in-Federated-Learning

  Implementation of Shuffled Model of Differential Privacy in Federated Learning." AISTATS, 2021.

  Language:Python173

• rois-codh/kmnist

  Repository for Kuzushiji-MNIST, Kuzushiji-49, and Kuzushiji-Kanji

  Language:Python69294

• zitengsun/hadamard_response

  Hadamard Response: Communication efficient, sample optimal, linear time locally private learning of distributions

  Language:Jupyter Notebook146

• optimization-for-data-driven-science/DP-with-public-data

  Optimal Differentially Private Learning with Public Data

  Language:Python51

• apple/ml-projunit

  Language:Python11

• WeiNingChen/Kashin-mean-estimation

  An implementation of Kashine based mean estimation shceme

  Language:Jupyter Notebook3

• FumiyukiKato/uldp-fl

  ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy

  Language:Python21

• SAP-samples/machine-learning-diff-private-federated-learning

  Simulate a federated setting and run differentially private federated learning.

  Language:Python35594

• git-disl/CLDP

  Secure and utility-aware data collection with condensed local differential privacy

  166

• FumiyukiKato/FL-TEE

  OLIVE: Oblivious and Differentially Private Federated Learning on TEE

  Language:C133

• wangbar0133/sunDP

  sunDP-master

  Language:Python32

• langnatalie/JoPEQ

  PyTorch implementation of Joint Privacy Enhancement and Quantization in Federated Learning (IEEE TSP 2023, IEEE ICASSP 2023, IEEE ISIT 2022)

  Language:Python14

• JeffffffFu/Awesome-Differential-Privacy-and-Meachine-Learning

  机器学习和差分隐私的论文笔记和代码仓

  Language:Python30731

• apple/ml-shuffling-amplification

  Language:Python1710

• zihangxiang/Practical-Differentially-Private-and-Byzantine-resilient-Federated

  Language:Python7

• zeyu-zh/TrustFL

  Language:C++194

• facebookresearch/FLSim

  Federated Learning Simulator (FLSim) is a flexible, standalone core library that simulates FL settings with a minimal, easy-to-use API. FLSim is domain-agnostic and accommodates many use cases such as vision and text.

  Language:Python24958

• marcoszh/BatchCrypt

  Language:Python7817