haochenuw
Providing easy access to privacy preserving technologies, such as FHE, MPC and E2EE. Projects include SEAL, Cupcake, and signaldemo.live.
MetaSeattle
Pinned Repositories
480.HW.2
math480hws
ABY
ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation
algorithms-in-SEAL
Some algorithms for computation on encrypted data based on Microsoft SEAL
coupgame
multiplayer coup game
eigen-mpc
privacy preserving eigenvalue decomposition
GaloisRLWE
MSR (Microsoft Research) internship project.
haochenuw.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
SealPIR
Example implementation of the SealPIR protocol
signal-demo
An interactive demo of the signal protocol, developed based on libsignal-typescript-demo
zero-modform
Computations of zero of modular forms
haochenuw's Repositories
haochenuw/algorithms-in-SEAL
Some algorithms for computation on encrypted data based on Microsoft SEAL
haochenuw/eigen-mpc
privacy preserving eigenvalue decomposition
haochenuw/signal-demo
An interactive demo of the signal protocol, developed based on libsignal-typescript-demo
haochenuw/coupgame
multiplayer coup game
haochenuw/haochenuw.github.io
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
haochenuw/SealPIR
Example implementation of the SealPIR protocol
haochenuw/ABY
ABY - A Framework for Efficient Mixed-protocol Secure Two-party Computation
haochenuw/akd
An implementation of an auditable key directory
haochenuw/concrete
Concrete Operates oN Ciphertexts Rapidly by Extending TfhE
haochenuw/CrypTen
A framework for Privacy Preserving Machine Learning
haochenuw/fbpcf
Private computation framework library allows developers to perform randomized controlled trials, without leaking information about who participated or what action an individual took. It uses secure multiparty computation to guarantee this privacy. It is suitable for conducting A/B testing, or measuring advertising lift and learning the aggregate statistics without sharing information on the individual level.
haochenuw/fbpcs
FBPCS (Facebook Private Computation Solutions) leverages secure multi-party computation (MPC) to output aggregated data without making unencrypted, readable data available to the other party or any third parties. Facebook provides impression & opportunity data, and the advertiser provides conversion / outcome data. Both parties have dedicated cloud computing instances living on separate Virtual Private Clouds (VPCs) that are connected to allow network communication. The FBPMP products that have been implemented are Private Lift and Private Attribution. It’s expected that more products will be implemented and added to the Private Measurement suite.
haochenuw/genCRTbuilder
general crt builder
haochenuw/JNITutorial
haochenuw/libsignal-typescript-fork
My fork of the libsignal typescript implementation. Tweaked some places for educational purposes.
haochenuw/linreg-mpc
A Secure Multiparty Computation (MPC) protocol for computing linear regression on vertically distributed datasets
haochenuw/manim-voiceover
Manim plugin for all things voiceover
haochenuw/opaque-ke
An implementation of the OPAQUE password-authenticated key exchange protocol
haochenuw/parity-challenge
haochenuw/pydeepstack
a python implementation of deepstack
haochenuw/Relinearization-Problem
haochenuw/relu_script
haochenuw/rust-cpace
A Rust implementation of CPace, a balanced PAKE.
haochenuw/seal-expand-example
haochenuw/selenium-github-actions
haochenuw/stunning-page
haochenuw/test-pages
haochenuw/test_git
haochenuw/tfhe
TFHE: Fast Fully Homomorphic Encryption Library over the Torus
haochenuw/website
this is the code for the sagemath.org website