awslabs / fast-differential-privacy

Related projects: ⓘ

• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆29Updated 2 years ago
• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆64Updated 7 months ago
• ftramer / Handcrafted-DP
  ☆77Updated 2 years ago
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆100Updated last year
• lxuechen / private-transformers
  A codebase that makes differentially private training of transformers easy.
  ☆151Updated last year
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆44Updated 5 years ago
• google-deepmind / jax_privacy
  Algorithms for Privacy-Preserving Machine Learning in JAX
  ☆87Updated 3 months ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆54Updated last year
• woodyx218 / opacus_global_clipping
  ☆13Updated last year
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆37Updated 2 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆52Updated last year
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆69Updated 3 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆129Updated last year
• huseyinatahaninan / Differentially-Private-Fine-tuning-of-Language-Models
  ☆61Updated 2 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• microsoft / dp-transformers
  Differentially-private transformers using HuggingFace and Opacus
  ☆108Updated 3 weeks ago
• google-research / public-data-in-dpfl
  ☆21Updated last year
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆84Updated last month
• microsoft / DPSDA
  [ICLR 2024] Generating DP Synthetic Data without Training
  ☆74Updated 5 months ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆47Updated 5 years ago
• AI-secure / G-PATE
  [NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators" by Yunhui Long*…
  ☆30Updated 2 years ago
• wanglun1996 / secure-robust-federated-learning
  ☆50Updated 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…
  ☆37Updated 5 months ago
• MinChen00 / UnlearningLeaks
  Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
  ☆45Updated 2 years ago
• git-disl / CPL_attack
  ☆34Updated 2 years ago
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆66Updated 7 months ago
• PatrickZH / Improved-Deep-Leakage-from-Gradients
  The code for "Improved Deep Leakage from Gradients" (iDLG).
  ☆139Updated 3 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 2 years ago
• zechenghe / Inverse_Collaborative_Inference
  ☆38Updated 3 years ago
• woodyx218 / Deep-Learning-with-GDP-Tensorflow
  Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"
  ☆47Updated 3 years ago