Alternatives and similar repositories for Secure-and-Private-AI

Users that are interested in Secure-and-Private-AI are comparing it to the libraries listed below

• DPBayes / ADADP
  Learning rate adaptation for differentially private stochastic gradient descent
  ☆17Updated 4 years ago
• alevine0 / DPA
  Code for the paper "Deep Partition Aggregation: Provable Defenses against General Poisoning Attacks"
  ☆12Updated 3 years ago
• kamathhrishi / PATE
  Pytorch implementation of paper Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data (https://arxiv.org/abs/16…
  ☆44Updated 3 years ago
• lingjuanlv / FPPDL
  code for TPDS paper "Towards Fair and Privacy-Preserving Federated Deep Models"
  ☆31Updated 3 years ago
• AmanPriyanshu / DP-HyperparamTuning
  DP-HyperparamTuning offers an array of tools for fast and easy hypertuning of various hyperparameters for the DP-SGD algorithm.
  ☆23Updated 3 years ago
• ebagdasa / differential-privacy-vs-fairness
  Code for "Differential Privacy Has Disparate Impact on Model Accuracy" NeurIPS'19
  ☆33Updated 4 years ago
• sam-yeom / ml-privacy-csf18
  Code for the CSF 2018 paper "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"
  ☆38Updated 6 years ago
• desternylin / perfed
  ☆16Updated last year
• watml / Fed-MGDA
  ☆29Updated 3 years ago
• git-disl / Fed-CDP
  Gradient-Leakage Resilient Federated Learning
  ☆13Updated 3 years ago
• BielStela / membership_inference
  Python package to create adversarial agents for membership inference attacks againts machine learning models
  ☆47Updated 6 years ago
• epfml / byzantine-robust-decentralized-optimizer
  ☆13Updated 3 years ago
• QinbinLi / FedKT
  Practical One-Shot Federated Learning for Cross-Silo Setting
  ☆42Updated 4 years ago
• superrrpotato / Defending-Neural-Backdoors-via-Generative-Distribution-Modeling
  The code is for our NeurIPS 2019 paper: https://arxiv.org/abs/1910.04749
  ☆34Updated 5 years ago
• ebagdasa / federated_adaptation
  Salvaging Federated Learning by Local Adaptation
  ☆56Updated last year
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆50Updated 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…
  ☆38Updated 3 years ago
• woodyx218 / Deep-Learning-with-GDP-Pytorch
  Code to accompany the paper "Deep Learning with Gaussian Differential Privacy"
  ☆33Updated 4 years ago
• dayu11 / Gradient-Embedding-Perturbation
  ☆27Updated 2 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆73Updated 4 years ago
• AI-secure / Shapley-Study
  [CVPR 2021] Scalability vs. Utility: Do We Have to Sacrifice One for the Other in Data Importance Quantification?
  ☆33Updated 4 years ago
• shenzebang / CENTAUR-Privacy-Federated-Representation-Learning
  A PyTorch based repository for Federate Learning with Differential Privacy
  ☆17Updated 2 years ago
• JunyiZhu-AI / R-GAP
  R-GAP: Recursive Gradient Attack on Privacy [Accepted at ICLR 2021]
  ☆37Updated 2 years ago
• amiratag / neuronshapley
  Code for "Neuron Shapley: Discovering the Responsible Neurons"
  ☆27Updated last year
• Jie-Ren / A-Unified-Game-Theoretic-Interpretation-of-Adversarial-Robustness
  The official implementation of A Unified Game-Theoretic Interpretation of Adversarial Robustness.
  ☆22Updated 3 years ago
• Sanghyun-Hong / DeepSloth
  [ICLR 2021: Spotlight] Source code for the paper "A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Infer…
  ☆15Updated 3 years ago
• DPBayes / DP-cross-silo-federated-learning
  Code for the DP cross-silo federated learning paper
  ☆11Updated 5 years ago
• andylamp / federated_pca
  Federated Principal Component Analysis Revisited!
  ☆43Updated 3 years ago
• hui-po-wang / ProgFed
  [ICML2022] ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
  ☆22Updated 2 years ago
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆19Updated 2 years ago