Alternatives and similar repositories for security-research-membership-inference-and-differential-privacy:

Users that are interested in security-research-membership-inference-and-differential-privacy are comparing it to the libraries listed below

• BielStela / membership_inference
  Python package to create adversarial agents for membership inference attacks againts machine learning models
  ☆46Updated 6 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆131Updated 2 years ago
• gitgik / differential-privacy-federated-learning
  Curated notebooks on how to train neural networks using differential privacy and federated learning.
  ☆67Updated 4 years ago
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆30Updated 2 years ago
• sam-yeom / ml-privacy-csf18
  Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
  ☆38Updated 6 years ago
• spliew / shuffgauss
  ☆10Updated last year
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆59Updated 5 years ago
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆142Updated 2 years ago
• Samuel-Maddock / federated-boosted-dp-trees
  Code for the CCS'22 paper "Federated Boosted Decision Trees with Differential Privacy"
  ☆44Updated last year
• inspire-group / membership-inference-evaluation
  Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
  ☆125Updated 11 months ago
• csong27 / membership-inference
  Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
  ☆193Updated 7 years ago
• Antonious-Grigis / Shuffled-Model-of-Differential-Privacy-in-Federated-Learning
  Implementation of Shuffled Model of Differential Privacy in Federated Learning." AISTATS, 2021.
  ☆17Updated 2 years ago
• kamathhrishi / PATE
  Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…
  ☆43Updated 3 years ago
• illidanlab / dpgan
  Source code of paper "Differentially Private Generative Adversarial Network"
  ☆69Updated 6 years ago
• Lory94 / dp-GAN
  Differentially Private Generative Adversarial Networks for Time Series, Continuous, and Discrete Open Data
  ☆33Updated 6 years ago
• jinyuan-jia / MemGuard
  ☆45Updated 5 years ago
• chamikara1986 / LDPFL
  Local Differential Privacy for Federated Learning
  ☆16Updated 2 years ago
• vaikkunth / PrivacyFL
  A Simulator for Privacy Preserving Federated Learning
  ☆93Updated 4 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• yashkant / model-inversion-attack
  Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…
  ☆83Updated 2 years ago
• woodyx218 / opacus_global_clipping
  ☆14Updated last year
• Samuel-Maddock / pure-LDP
  Python package for simple implementations of state-of-the-art LDP frequency estimation algorithms. Contains code for our VLDB 2021 Paper.
  ☆74Updated last year
• thecml / dpsgd-optimizer
  Amortized version of the differentially private SGD algorithm published in "Deep Learning with Differential Privacy" by Abadi et al. Enfo…
  ☆41Updated 11 months ago
• maxencenoble / Differential-Privacy-for-Heterogeneous-Federated-Learning
  Differentially Private Federated Learning on Heterogeneous Data
  ☆61Updated 3 years ago
• woodyx218 / Deep-Learning-with-GDP-Pytorch
  Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"
  ☆33Updated 3 years ago
• ebagdasa / differential-privacy-vs-fairness
  Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19
  ☆34Updated 3 years ago
• ppmlguy / DP-AGD
  Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget
  ☆49Updated 7 years ago
• ssg-research / WAFFLE
  WAFFLE: Watermarking in Federated Learning
  ☆18Updated last year
• BorjaBalle / amplification-by-shuffling
  Implementation of calibration bounds for differential privacy in the shuffle model
  ☆23Updated 4 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year