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.
  ☆76Updated last year
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆111Updated 3 years ago
• JonasGeiping / breaching
  Breaching privacy in federated learning scenarios for vision and text
  ☆310Updated last week
• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆32Updated 3 years ago
• lxuechen / private-transformers
  A codebase that makes differentially private training of transformers easy.
  ☆182Updated 3 years ago
• ftramer / Handcrafted-DP
  ☆80Updated 3 years ago
• microsoft / DPSDA
  Private Evolution: Generating DP Synthetic Data without Training [ICLR 2024, ICML 2024 Spotlight]
  ☆109Updated 2 months ago
• HongshengHu / membership-inference-machine-learning-literature
  ☆369Updated last week
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆43Updated 4 years ago
• JonasGeiping / invertinggradients
  Algorithms to recover input data from their gradient signal through a neural network
  ☆311Updated 2 years ago
• google-research / DP-FTRL
  DP-FTRL from "Practical and Private (Deep) Learning without Sampling or Shuffling" for centralized training.
  ☆35Updated 7 months ago
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆65Updated last year
• wanglun1996 / secure-robust-federated-learning
  ☆55Updated 2 years ago
• microsoft / dp-transformers
  Differentially-private transformers using HuggingFace and Opacus
  ☆145Updated last year
• woodyx218 / opacus_global_clipping
  ☆15Updated 2 years ago
• yuxiangw / autodp
  autodp: A flexible and easy-to-use package for differential privacy
  ☆278Updated 2 years ago
• cleverhans-lab / machine-unlearning
  ☆195Updated 2 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆60Updated 6 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…
  ☆42Updated last year
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆51Updated 7 years ago
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆72Updated last year
• huseyinatahaninan / Differentially-Private-Fine-tuning-of-Language-Models
  ☆77Updated 3 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆57Updated 2 years ago
• zechenghe / Inverse_Collaborative_Inference
  ☆51Updated 4 years ago
• ksreenivasan / OOD_Federated_Learning
  ☆72Updated 3 years ago
• inspire-group / membership-inference-evaluation
  Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
  ☆132Updated last year
• facebookresearch / dp_compression
  Code repo for the paper "Privacy-aware Compression for Federated Data Analysis".
  ☆19Updated 2 years ago
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆152Updated 3 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆135Updated 3 years ago