Alternatives and similar repositories for fast-differential-privacy:

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

• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆73Updated last year
• lxuechen / private-transformers
  A codebase that makes differentially private training of transformers easy.
  ☆171Updated 2 years ago
• microsoft / dp-transformers
  Differentially-private transformers using HuggingFace and Opacus
  ☆133Updated 6 months ago
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆30Updated 2 years ago
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆106Updated 2 years ago
• microsoft / DPSDA
  Private Evolution: Generating DP Synthetic Data without Training [ICLR 2024, ICML 2024 Spotlight]
  ☆90Updated last month
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆58Updated 5 months ago
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆46Updated 6 years ago
• ftramer / Handcrafted-DP
  ☆80Updated 2 years ago
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• huseyinatahaninan / Differentially-Private-Fine-tuning-of-Language-Models
  ☆71Updated 2 years ago
• 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
• woodyx218 / opacus_global_clipping
  ☆13Updated last year
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆58Updated 5 years ago
• AI-secure / aug-pe
  [ICML 2024 Spotlight] Differentially Private Synthetic Data via Foundation Model APIs 2: Text
  ☆33Updated 2 months ago
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆38Updated 3 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆131Updated 2 years ago
• wanglun1996 / secure-robust-federated-learning
  ☆54Updated 2 years ago
• BorjaBalle / amplification-by-shuffling
  Implementation of calibration bounds for differential privacy in the shuffle model
  ☆23Updated 4 years ago
• Billy1900 / Awesome-Differential-Privacy
  Differential private machine learning
  ☆191Updated 3 years ago
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆69Updated last year
• google-deepmind / jax_privacy
  Algorithms for Privacy-Preserving Machine Learning in JAX
  ☆92Updated 9 months ago
• yuxiangw / autodp
  autodp: A flexible and easy-to-use package for differential privacy
  ☆273Updated last year
• MinChen00 / UnlearningLeaks
  Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
  ☆46Updated 2 years ago
• git-disl / CPL_attack
  ☆35Updated 3 years ago
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆88Updated 7 months ago
• facebookresearch / dp_compression
  Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".
  ☆18Updated last year
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year
• ganevgv / dp-generative-models
  Differentially Private (tabular) Generative Models Papers with Code
  ☆47Updated 8 months ago
• google-research / DP-FTRL
  DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.
  ☆28Updated 7 months ago