Alternatives and similar repositories for python-sslib:

Users that are interested in python-sslib are comparing it to the libraries listed below

• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆130Updated 2 years ago
• csong27 / property-inference-collaborative-ml
  Code for Exploiting Unintended Feature Leakage in Collaborative Learning (in Oakland 2019)
  ☆53Updated 5 years ago
• lapets / shamirs
  Minimal pure-Python implementation of Shamir's secret sharing scheme.
  ☆11Updated last month
• ssg-research / WAFFLE
  WAFFLE: Watermarking in Federated Learning
  ☆17Updated last year
• csong27 / ml-model-remember
  Code for Machine Learning Models that Remember Too Much (in CCS 2017)
  ☆30Updated 7 years ago
• lucifer2859 / Paillier-LWE-based-PHE
  Privacy-Preserving Deep Learning via Additively Homomorphic Encryption
  ☆68Updated 4 years ago
• Shubha23 / Laplace-and-Exponential-mechanisms-for-privacy
  Applying Laplace and exponential mechanisms to add random noise to data for differential privacy. Plotting MSE vs. epsilon.
  ☆28Updated 4 years ago
• pps-lab / rofl-project-code
  ☆33Updated last year
• SSGAalto / minionn
  Privacy -preserving Neural Networks
  ☆78Updated 6 years ago
• v4va / FedShare
  FedShare: Secure Aggregation based on Additive Secret Sharing in Federated Learning
  ☆18Updated 2 years ago
• HikariX / MP-FedXGB
  An Efficient Learning Framework For Federated XGBoostUsing Secret Sharing And Distributed Optimization
  ☆30Updated 3 years ago
• IBM / discrete-gaussian-differential-privacy
  Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010
  ☆59Updated 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
• ftramer / Handcrafted-DP
  ☆80Updated 2 years ago
• DPBayes / dca-nips2017
  Differentially private learning on distributed data (NIPS 2017)
  ☆12Updated 7 years ago
• lmunoz-gonzalez / Poisoning-Attacks-with-Back-gradient-Optimization
  Example of the attack described in the paper "Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization"
  ☆21Updated 5 years ago
• dpcomp-org / dpcomp_core
  ☆43Updated 3 years ago
• corentingiraud / federated-learning-secure-aggregation
  A simple Python implementation of a secure aggregation protocole for federated learning.
  ☆35Updated last year
• yashkant / model-inversion-attack
  Implementation of the Model Inversion Attack introduced with Model Inversion Attacks that Exploit Confidence Information and Basic Counte…
  ☆82Updated last year
• DashanGao / Secret-sharing-based_2PC_vertical_fed_learning
  MPC Secure Multiparty Computation. A three-party secret-sharing-based vertical federated learning setting. The data are vertically parti…
  ☆23Updated 5 years ago
• intel / pailliercryptolib_python
  Intel Paillier Cryptosystem Library is an open-source library which provides accelerated performance of a partial homomorphic encryption …
  ☆56Updated 7 months ago
• fushuhao6 / Attack-Resistant-Federated-Learning
  ☆31Updated 4 years ago
• csiro-mlai / deep-mpc
  ☆36Updated 3 months ago
• LaRiffle / ariann
  Low-Interaction Privacy-Preserving Deep Learning via Function Secret Sharing
  ☆52Updated 3 years ago
• schoppmp / linreg-mpc
  A Secure Multiparty Computation (MPC) protocol for computing linear regression on vertically distributed datasets.
  ☆32Updated 5 years ago
• ppml-news / ppml-news.github.io
  News in Privacy-Preserving Machine Learning
  ☆12Updated 5 years ago
• mortendahl / awesome-ppml
  A curated list of resources for privacy-preserving machine learning
  ☆148Updated 3 years ago
• inspire-group / privacy-vs-robustness
  Privacy Risks of Securing Machine Learning Models against Adversarial Examples
  ☆44Updated 5 years ago
• eniac / flamingo
  A secure aggregation system for private federated learning
  ☆39Updated 9 months ago
• mkucz95 / encrypted_ai_finance
  Credit Approval Classification Deep Learning Model using Differential Drivacy, Secure Multi-Party Computation, and Federated Learning
  ☆24Updated 2 years ago