Alternatives and similar repositories for label_dp_antipodes:

Users that are interested in label_dp_antipodes are comparing it to the libraries listed below

• jagielski / auditing-dpsgd
  Code for Auditing DPSGD
  ☆37Updated 3 years ago
• woodyx218 / opacus_global_clipping
  ☆14Updated last year
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆106Updated 2 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
• BorjaBalle / amplification-by-shuffling
  Implementation of calibration bounds for differential privacy in the shuffle model
  ☆23Updated 4 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆131Updated 2 years ago
• ppmlguy / DP-AGD
  Concentrated Differentially Private Gradient Descent with Adaptive per-iteration Privacy Budget
  ☆49Updated 7 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆59Updated 5 years ago
• ftramer / Handcrafted-DP
  ☆80Updated 2 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆58Updated 5 months ago
• jinyuan-jia / MemGuard
  ☆45Updated 5 years ago
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆64Updated 3 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
• epfml / byzantine-robust-optimizer
  Learning from history for Byzantine Robustness
  ☆22Updated 3 years ago
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆19Updated 2 years ago
• wanglun1996 / secure-robust-federated-learning
  ☆54Updated 2 years ago
• woodyx218 / Deep-Learning-with-GDP-Tensorflow
  Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"
  ☆49Updated 3 years ago
• BorjaBalle / analytic-gaussian-mechanism
  Analytic calibration for differential privacy with Gaussian perturbations
  ☆46Updated 6 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆71Updated 3 years ago
• gdisag / gradient_disaggregation
  ☆14Updated last year
• AI-secure / G-PATE
  [NeurIPS 2021] "G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators" by Yunhui Long*…
  ☆30Updated 3 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• xinjianluo / featureinference-vfl
  ☆25Updated 3 years ago
• ksreenivasan / OOD_Federated_Learning
  ☆69Updated 2 years ago
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆88Updated 7 months ago
• jeremy43 / Private_kNN
  ☆24Updated 2 years ago
• inspire-group / ModelPoisoning
  Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
  ☆147Updated 2 years ago
• microsoft / prv_accountant
  A fast algorithm to optimally compose privacy guarantees of differentially private (DP) mechanisms to arbitrary accuracy.
  ☆73Updated last year