A unified framework for privacy-preserving data analysis and machine learning

Privacy-Preserving Computing Platform 由密码学专家团队打造的开源隐私计算平台，支持多方安全计算、联邦学习、隐私求交、匿踪查询等。

SPU (Secure Processing Unit) aims to be a provable, measurable secure computation device, which provides computation ability while keeping your private data protected.

Privacy Preserving Vertical Federated Learning

Awesome list for cryptographic secure computation paper. This repo includes *Lattice*, *DifferentialPrivacy*, *MPC* and also a comprehensive summary for top conferences.

Private Set Intersection Cardinality protocol based on ECDH and Bloom Filters

Private set intersection implemented in Python

The repo of Private Set Intersection(PSI) and Private Information Retrieval(PIR) from SecretFlow.

Bridge between TensorFlow and Google's Private Join and Compute library

Python wrapper for labeled and unlabeled asymmetric private set intersection (APSI).

A Go implementation of the protocols for {MPSI, MPSIU, MPSI-Sum, MPSIU-Sum} described in Estimating Incidental Collection in Foreign Intelligence Surveillance: Large-Scale Multiparty Private Set Intersection with Union and Sum.

TCN protocol based on Private Set Intersection Cardinality.

Updatable Private Set Intersection Revisited: Extended Functionalities, Deletion, and Worst-Case Complexity (Asiacrypt 2024)

Private Set Intersection algorithm written with pure java.

Private Set Intersection protocols

Easy PSI is a web platform focused on Private Set Intersection(PSI), which is based on Kuscia and SecretFlow PSI Library.

Privacy enhancing technologies (PETs) playground.

A Kotlin library for private set intersection

Private Set Intersection (PSI) is a famous secure two-party computation (2PC) problem where two parties (client and server) want to jointly compute the intersection of their inputs sets without revealing additional information about the input sets. Our implementation offers open-source research code to perform fast privacy-preserving set interse...

Meet friends from specific interests through the privacy-preserving P2P Coincidence network.