A unified framework for privacy-preserving data analysis and machine learning
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Updated
Sep 11, 2024 - Python
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
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.
Awesome list for cryptographic secure computation paper. This repo includes *Lattice*, *DifferentialPrivacy*, *MPC* and also a comprehensive summary for top conferences.
Python wrapper for labeled and unlabeled asymmetric private set intersection (APSI).
Easy PSI is a web platform focused on Private Set Intersection(PSI), which is based on Kuscia and SecretFlow PSI Library.
Bridge between TensorFlow and Google's Private Join and Compute library
Private Set Intersection protocols
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.
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...
TCN protocol based on Private Set Intersection Cardinality.
Privacy enhancing technologies (PETs) playground.
Meet friends from specific interests through the privacy-preserving P2P Coincidence network.
A fast implementation of ECC-based Private Set Intersection
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