Alternatives and similar repositories for G-PATE:

Users that are interested in G-PATE are comparing it to the libraries listed below

• facebookresearch / label_dp_antipodes
  Code to reproduce experiments in "Antipodes of Label Differential Privacy PATE and ALIBI"
  ☆31Updated 3 years ago
• reihaneh-torkzadehmahani / DP-CGAN
  Differentially Private Conditional Generative Adversarial Network
  ☆31Updated 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 3 years ago
• ParkLabML / DP-MERF
  ☆17Updated 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 last year
• DingfanChen / GS-WGAN
  Official implementation of "GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators" (NeurIPS 2020)
  ☆69Updated 2 years ago
• csong27 / auditing-text-generation
  Code for Auditing Data Provenance in Text-Generation Models (in KDD 2019)
  ☆9Updated 5 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
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆19Updated 2 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆60Updated 5 years ago
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆108Updated 2 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆60Updated 7 months ago
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆28Updated 2 years ago
• ganevgv / dp-generative-models
  Differentially Private (tabular) Generative Models Papers with Code
  ☆48Updated 10 months ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆49Updated 2 years ago
• frhrdr / patepy
  A simple wrapper for the PATE analysis for Differential Privacy by Papernot, Song et al.
  ☆9Updated 5 years ago
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆47Updated 6 years ago
• woodyx218 / Deep-Learning-with-GDP-Pytorch
  Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"
  ☆33Updated 4 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
• haiphanNJIT / SecureSGD
  Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness (IJCAI'19).
  ☆13Updated 4 years ago
• jeremy43 / Private_kNN
  ☆24Updated 3 years ago
• DingfanChen / GAN-Leaks
  Official implementation of "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models" (CCS 2020)
  ☆47Updated 3 years ago
• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• woodyx218 / opacus_global_clipping
  ☆15Updated last year
• BielStela / membership_inference
  Python package to create adversarial agents for membership inference attacks againts machine learning models
  ☆47Updated 6 years ago
• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆73Updated last year
• dayu11 / Improved-DP-SGD
  Improved DP-SGD for optimizing
  ☆18Updated 6 years ago
• spring-epfl / mia
  A library for running membership inference attacks against ML models
  ☆144Updated 2 years ago
• awslabs / fast-differential-privacy
  Fast, memory-efficient, scalable optimization of deep learning with differential privacy
  ☆120Updated 4 months ago