Alternatives and similar repositories for MIA

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

• AdrienBenamira / membership_inference_attack
  Implementation of the paper : "Membership Inference Attacks Against Machine Learning Models", Shokri et al.
  ☆60Updated 6 years ago
• MinChen00 / UnlearningLeaks
  Official implementation of "When Machine Unlearning Jeopardizes Privacy" (ACM CCS 2021)
  ☆50Updated 3 years ago
• DingfanChen / RelaxLoss
  Official implementation of "RelaxLoss: Defending Membership Inference Attacks without Losing Utility" (ICLR 2022)
  ☆48Updated 3 years ago
• shrebox / Privacy-Attacks-in-Machine-Learning
  Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
  ☆65Updated last year
• cchoquette / membership-inference
  Code for the paper: Label-Only Membership Inference Attacks
  ☆67Updated 4 years ago
• inspire-group / membership-inference-evaluation
  Systematic Evaluation of Membership Inference Privacy Risks of Machine Learning Models
  ☆132Updated last year
• alewarne / MachineUnlearning
  Code related to the paper "Machine Unlearning of Features and Labels"
  ☆72Updated last year
• dayu11 / Differentially-Private-Deep-Learning
  This repo implements several algorithms for learning with differential privacy.
  ☆111Updated 3 years ago
• liuyugeng / ML-Doctor
  Code for ML Doctor
  ☆92Updated last year
• ybdai7 / Chameleon-durable-backdoor
  [ICML 2023] Official code implementation of "Chameleon: Adapting to Peer Images for Planting Durable Backdoors in Federated Learning (htt…
  ☆43Updated 3 months ago
• ganeshdg95 / Leveraging-Adversarial-Examples-to-Quantify-Membership-Information-Leakage
  ☆19Updated 2 years ago
• jinyuan-jia / MemGuard
  ☆46Updated 6 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
  ☆60Updated last year
• ksreenivasan / OOD_Federated_Learning
  ☆71Updated 3 years ago
• zhuohangli / GGL
  A pytorch implementation of the paper "Auditing Privacy Defenses in Federated Learning via Generative Gradient Leakage".
  ☆62Updated 3 years ago
• Machine-Learning-Security-Lab / mia_prune
  Membership Inference Attacks and Defenses in Neural Network Pruning
  ☆28Updated 3 years 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".
  ☆35Updated 3 years ago
• pasquini-dario / SplitNN_FSHA
  Privacy attacks on Split Learning
  ☆43Updated 4 years ago
• csong27 / membership-inference
  Code for Membership Inference Attack against Machine Learning Models (in Oakland 2017)
  ☆200Updated 8 years ago
• jeremy313 / Soteria
  Official implementation of "Provable Defense against Privacy Leakage in Federated Learning from Representation Perspective"
  ☆57Updated 2 years ago
• jgshu / Attacks-and-Defenses-in-Federated-Learning
  ☆38Updated 4 years ago
• HongshengHu / membership-inference-machine-learning-literature
  ☆367Updated this week
• jhcknzzm / Federated-Learning-Backdoor
  ICML 2022 code for "Neurotoxin: Durable Backdoors in Federated Learning" https://arxiv.org/abs/2206.10341
  ☆80Updated 2 years ago
• zaixizhang / FLDetector
  The official code of KDD22 paper "FLDetecotor: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clien…
  ☆84Updated 2 years ago
• AI-secure / CRFL
  CRFL: Certifiably Robust Federated Learning against Backdoor Attacks (ICML 2021)
  ☆74Updated 4 years ago
• wanglun1996 / secure-robust-federated-learning
  ☆55Updated 2 years ago
• TASI-LAB / Unlearning-inversion-attacks
  This repository contains Python code for the paper "Learn What You Want to Unlearn: Unlearning Inversion Attacks against Machine Unlearni…
  ☆19Updated last year
• qzzqzzb / A3FL
  ☆30Updated 2 years ago
• AhmedSalem2 / ML-Leaks
  Code for the paper "ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models"
  ☆85Updated 4 years ago
• AI-secure / DataLens
  [CCS 2021] "DataLens: Scalable Privacy Preserving Training via Gradient Compression and Aggregation" by Boxin Wang*, Fan Wu*, Yunhui Long…
  ☆36Updated 4 years ago