Alternatives and similar repositories for differential-privacy-vs-fairness:

Users that are interested in differential-privacy-vs-fairness are comparing it to the libraries listed below

• 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
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 2 years ago
• shrezaei / MI-Attack
  ☆31Updated 4 months ago
• google-research / public-data-in-dpfl
  ☆22Updated 2 years ago
• inspire-group / privacy-vs-robustness
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples
  ☆44Updated 5 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆130Updated 2 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• kamathhrishi / PATE
  Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…
  ☆42Updated 3 years ago
• ebagdasa / federated_adaptation
  Salvaging Federated Learning by Local Adaptation
  ☆56Updated 6 months ago
• ftramer / Handcrafted-DP
  ☆80Updated 2 years ago
• krishnap25 / simplicial-fl
  Simplicial-FL to manage client device heterogeneity in Federated Learning
  ☆22Updated last year
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆20Updated last year
• lingjuanlv / FPPDL
  code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"
  ☆31Updated 2 years ago
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆142Updated 2 years ago
• wuyinjun-1993 / DeltaGrad
  ☆28Updated 3 years ago
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆30Updated 2 years ago
• BielStela / membership_inference
  Python package to create adversarial agents for membership inference attacks againts machine learning models
  ☆46Updated 5 years ago
• AI-secure / DataLens
  [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…
  ☆37Updated 3 years ago
• SPIN-UMass / ML-Privacy-Regulization
  ☆26Updated 6 years ago
• woodyx218 / opacus_global_clipping
  ☆13Updated last year
• inspire-group / ModelPoisoning
  Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
  ☆147Updated 2 years ago
• DPBayes / ADADP
  Learning rate adaptation for differentially private stochastic gradient descent
  ☆16Updated 3 years ago
• jinyuan-jia / MemGuard
  ☆45Updated 5 years ago
• google-deepmind / jax_privacy
  Algorithms for Privacy-Preserving Machine Learning in JAX
  ☆92Updated 7 months ago
• dayu11 / Gradient-Embedding-Perturbation
  ☆27Updated 2 years ago
• lee-man / federated-learning
  Related material on Federated Learning
  ☆26Updated 4 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆71Updated 3 years ago
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆46Updated 6 years ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 2 years ago