Язык желательно Go, но не критично.
ChatGPT выдал следущие варианты:
Secure Multi-Party Computation (SMPC) is a field of cryptography that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. There are several software libraries and frameworks available for implementing SMPC protocols. The choice of the best library depends on your specific use case, programming language preference, and performance requirements. Here are some of the notable SMPC libraries and frameworks:
PySyft (PyTorch):
PySyft is an open-source framework built on PyTorch for privacy-preserving machine learning. It offers a wide range of tools for
secure multi-party computation, including federated learning and differential privacy.
TenSEAL (PyTorch):
TenSEAL is a library built on PyTorch specifically for Secure Homomorphic Encryption. It provides a high-level API for working with encrypted data and performing secure computations.
PySEAL (Microsoft SEAL):
Microsoft SEAL (Simple Encrypted Arithmetic Library) is a popular open-source library for homomorphic encryption. PySEAL is the
Python wrapper for SEAL, allowing you to perform operations on encrypted data securely.
PALISADE (C++):
PALISADE is an open-source C++ library for lattice-based cryptography, which is the foundation for many SMPC protocols. It provides a wide range of cryptographic primitives for building secure applications.
SCALE-MAMBA (Python):
SCALE-MAMBA is a Python library for secure multi-party computation. It includes a compiler that can convert high-level code into secure protocols, making it easier to define and execute secure computations.
mpc.js (JavaScript):
If you're interested in web-based applications with SMPC, mpc.js is a JavaScript library for secure multi-party computation. It allows you to perform computations in the browser securely.
MPC-Toolkit (Java):
MPC-Toolkit is a Java-based library for secure multi-party computation. It provides a range of protocols and cryptographic operations for building secure applications.
Scapi (Java):
Scapi is a Java-based library that focuses on secure computation protocols. It includes a wide range of cryptographic primitives and supports various secure computation techniques.
Empirical (Rust):
Empirical is a Rust-based library for secure multi-party computation. It aims to provide high-performance implementations of SMPC protocols.
ObliVM (Python):
ObliVM is a Python library for secure two-party and multi-party computation. It's designed for privacy-preserving machine learning applications and supports both semi-honest and malicious security models.
mpc.js и ObliVM - вообще непонятно поддерживаются ли.
PySyft без нормальной документации.
Ну и т.д.
Что посоветуете, кто писал?
ChatGPT выдал следущие варианты:
Secure Multi-Party Computation (SMPC) is a field of cryptography that enables multiple parties to jointly compute a function over their inputs while keeping those inputs private. There are several software libraries and frameworks available for implementing SMPC protocols. The choice of the best library depends on your specific use case, programming language preference, and performance requirements. Here are some of the notable SMPC libraries and frameworks:
PySyft (PyTorch):
PySyft is an open-source framework built on PyTorch for privacy-preserving machine learning. It offers a wide range of tools for
secure multi-party computation, including federated learning and differential privacy.
TenSEAL (PyTorch):
TenSEAL is a library built on PyTorch specifically for Secure Homomorphic Encryption. It provides a high-level API for working with encrypted data and performing secure computations.
PySEAL (Microsoft SEAL):
Microsoft SEAL (Simple Encrypted Arithmetic Library) is a popular open-source library for homomorphic encryption. PySEAL is the
Python wrapper for SEAL, allowing you to perform operations on encrypted data securely.
PALISADE (C++):
PALISADE is an open-source C++ library for lattice-based cryptography, which is the foundation for many SMPC protocols. It provides a wide range of cryptographic primitives for building secure applications.
SCALE-MAMBA (Python):
SCALE-MAMBA is a Python library for secure multi-party computation. It includes a compiler that can convert high-level code into secure protocols, making it easier to define and execute secure computations.
mpc.js (JavaScript):
If you're interested in web-based applications with SMPC, mpc.js is a JavaScript library for secure multi-party computation. It allows you to perform computations in the browser securely.
MPC-Toolkit (Java):
MPC-Toolkit is a Java-based library for secure multi-party computation. It provides a range of protocols and cryptographic operations for building secure applications.
Scapi (Java):
Scapi is a Java-based library that focuses on secure computation protocols. It includes a wide range of cryptographic primitives and supports various secure computation techniques.
Empirical (Rust):
Empirical is a Rust-based library for secure multi-party computation. It aims to provide high-performance implementations of SMPC protocols.
ObliVM (Python):
ObliVM is a Python library for secure two-party and multi-party computation. It's designed for privacy-preserving machine learning applications and supports both semi-honest and malicious security models.
mpc.js и ObliVM - вообще непонятно поддерживаются ли.
PySyft без нормальной документации.
Ну и т.д.
Что посоветуете, кто писал?