uvm-plaid / chorus

Related projects ⓘ

Alternatives and complementary repositories for chorus

• ektelo / ektelo
  ☆38Updated 2 years ago
• ucbrise / waldo
  ☆25Updated last year
• IBM / discrete-gaussian-differential-privacy
  Code for Canonne-Kamath-Steinke paper https://arxiv.org/abs/2004.00010
  ☆59Updated 4 years ago
• dpcomp-org / dpcomp_core
  ☆43Updated 3 years ago
• SSGAalto / minionn
  Privacy -preserving Neural Networks
  ☆75Updated 5 years ago
• SabaEskandarian / ObliDB
  An Oblivious General-Purpose SQL Database for the Cloud
  ☆56Updated 4 years ago
• bargavj / distributedMachineLearning
  This work combines differential privacy and multi-party computation protocol to achieve distributed machine learning.
  ☆26Updated 4 years ago
• schoppmp / linreg-mpc
  A Secure Multiparty Computation (MPC) protocol for computing linear regression on vertically distributed datasets.
  ☆32Updated 5 years ago
• mc2-project / secure-xgboost
  Secure collaborative training and inference for XGBoost.
  ☆105Updated 2 years ago
• uvm-plaid / duet
  Duet: A Language for Differential Privacy
  ☆17Updated 2 years ago
• cmla-psu / statdp
  Statistical Counterexample Detector for Differential Privacy
  ☆28Updated 7 months ago
• mc2-project / cerebro
  Cerebro: A platform for Secure Coopetitive Learning
  ☆20Updated 3 years ago
• opendp / dpcreator
  Web application that makes data releases that satisfy differential privacy using the OpenDP Library
  ☆20Updated 3 months ago
• smcql / smcql
  ☆53Updated 9 months ago
• marcoszh / BatchCrypt
  ☆78Updated 4 years ago
• ucbrise / dory
  ☆33Updated 4 years ago
• hharcolezi / multi-freq-ldpy
  Multiple Frequency Estimation Under Local Differential Privacy in Python
  ☆41Updated last year
• emp-toolkit / emp-sh2pc
  Semi-honest Two Party Computation Based on Garbled Circuits.
  ☆76Updated 2 years ago
• facebookresearch / Opacus-lab
  Research and experimental code related to Opacus, an open-source library for training PyTorch models with Differential Privacy
  ☆17Updated last month
• FudanMPL / SecMML
  SecMML (Queqiao): Secure MPC (multi-party computation) Machine Learning Framework.
  ☆111Updated last year
• SpiRITlab / SparkFHE-Examples
  SparkFHE project demo examples
  ☆20Updated 2 years ago
• bargavj / EvaluatingDPML
  This project's goal is to evaluate the privacy leakage of differentially private machine learning models.
  ☆129Updated last year
• multiparty / conclave
  Query compiler for secure multi-party computation.
  ☆98Updated 4 years ago
• sarus-tech / dp-xgboost
  ☆23Updated 10 months ago
• CASP-Systems-BU / Secrecy
  A relational Multi-Party Computation framework for analytics in untrusted clouds
  ☆24Updated 3 months ago
• mortendahl / awesome-ppml
  A curated list of resources for privacy-preserving machine learning
  ☆147Updated 3 years ago
• awslabs / privacy-preserving-xgboost-inference
  Privacy-preserving XGBoost Inference
  ☆48Updated last year
• capeprivacy / tf-trusted
  tf-trusted allows you to run TensorFlow models in secure enclaves
  ☆87Updated 4 years ago
• OpenMined / SyMPC
  A SMPC companion library for Syft
  ☆100Updated last week
• mcoder / private-set-intersection
  A python library providing several polynomial-based Private Set Intersection protocols.
  ☆43Updated 10 years ago