Alternatives and similar repositories for awesome-privacy-preserving-machine-learning:

Users that are interested in awesome-privacy-preserving-machine-learning are comparing it to the libraries listed below

• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆55Updated last year
• AI-secure / DBA
  DBA: Distributed Backdoor Attacks against Federated Learning (ICLR 2020)
  ☆186Updated 3 years ago
• purp1eHaze / FedIPR
  Webank AI
  ☆42Updated last month
• ksreenivasan / OOD_Federated_Learning
  ☆69Updated 2 years ago
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆88Updated 7 months ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆71Updated 3 years ago
• jhcknzzm / Federated-Learning-Backdoor
  ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341
  ☆70Updated 2 years ago
• shaneson0 / attacking_federate_learning
  基于《A Little Is Enough: Circumventing Defenses For Distributed Learning》的联邦学习攻击模型
  ☆62Updated 4 years ago
• inspire-group / ModelPoisoning
  Code for "Analyzing Federated Learning through an Adversarial Lens" https://arxiv.org/abs/1811.12470
  ☆147Updated 2 years ago
• ebagdasa / pytorch-privacy
  simple Differential Privacy in PyTorch
  ☆48Updated 4 years ago
• PatrickZH / Improved-Deep-Leakage-from-Gradients
  The code for "Improved Deep Leakage from Gradients" (iDLG).
  ☆148Updated 4 years ago
• FederatedAI / research
  ☆172Updated 5 months ago
• ebagdasa / backdoor_federated_learning
  Source code for paper "How to Backdoor Federated Learning" (https://arxiv.org/abs/1807.00459)
  ☆291Updated 8 months ago
• siquanhuang / Multi-metrics
  Multi-metrics adaptively identifies backdoors in Federated learning
  ☆24Updated last year
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 2 years ago
• DeRafael / CAFE
  ☆21Updated 3 years ago
• KaiyuanZh / FLIP
  [ICLR 2023, Best Paper Award at ECCV’22 AROW Workshop] FLIP: A Provable Defense Framework for Backdoor Mitigation in Federated Learning
  ☆53Updated 3 months ago
• ChanChiChoi / awesome-Federated-Learning
  federated-learning
  ☆82Updated 2 years ago
• zhmzm / Poisoning_Backdoor-critical_Layers_Attack
  [ICLR2024] "Backdoor Federated Learning by Poisoning Backdoor-Critical Layers"
  ☆32Updated 3 months ago
• DistributedML / FoolsGold
  A sybil-resilient distributed learning protocol.
  ☆103Updated last year
• zhuohangli / GGL
  A pytorch implementation of the paper "Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage".
  ☆57Updated 2 years ago
• csong27 / membership-inference
  Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
  ☆193Updated 7 years ago
• Rand2AI / GRNN
  Official implementation of "GRNN: Generative Regression Neural Network - A Data Leakage Attack for Federated Learning"
  ☆30Updated 3 years ago
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆69Updated last year
• jinghuichen / Focused-Flip-Federated-Backdoor-Attack
  Github Repo for AAAI 2023 paper: On the Vulnerability of Backdoor Defenses for Federated Learning
  ☆37Updated last year
• krishnap25 / RFA
  Robust aggregation for federated learning with the RFA algorithm.
  ☆48Updated 2 years ago
• Jaskiee / GAN-Attack-against-Federated-Deep-Learning
  The reproduction of the paper Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning.
  ☆61Updated 2 years ago
• vrt1shjwlkr / NDSS21-Model-Poisoning
  Code for NDSS 2021 Paper "Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses Against Federated Learning"
  ☆142Updated 2 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆58Updated 5 months ago
• TinfoilHat0 / Defending-Against-Backdoors-with-Robust-Learning-Rate
  The code of AAAI-21 paper titled "Defending against Backdoors in Federated Learning with Robust Learning Rate".
  ☆31Updated 2 years ago