Privacy Computing Network

Privacy Computing Network

Enigma, Oasis, Phala, and OpenMined all provide secure computing solutions. Enigma, Oasis, and Phala are designed for general computing, while OpenMinded focuses on privacy machine learning. Enigma, Oasis, and Phala adopt a confidential computing scheme based on TEE, while OpenMinded mainly uses federated learning advocated by Google and differential privacy advocated by Apple. The Algorithmia project utilizes blockchain to implement an interactive machine learning model marketplace, which is actually achieved through model transactions using smart contracts.
Layer2 privacy computing network is one of the important directions for the development of blockchain technology. It provides privacy computing capabilities at the Layer2 level, aiming to solve problems such as data circulation, privacy protection, and secure computing. The following are detailed extensions and explanations of the four privacy computing network projects, Enigma, Oasis, Phala, and OpenMined:

  1. Enigma/Secret Network

Project background: Enigma was initially founded at MIT in 2015 to solve the Internet privacy crisis. In 2020, the project was upgraded to Secret Network, becoming the first layer blockchain to support universal private computing.

Technical features: Secret Network is based on decentralized protocols and privacy first products, utilizing Trusted Execution Environment (TEE) to compute encrypted data without touching unencrypted raw data. It allows applications to use encrypted data instead of exposing it on the chain, providing provable and programmable privacy protection.

Application scenario: Secret Network supports various privacy computing applications, such as cross chain projects SecretSwap, privacy NFTs, etc. In addition, it has made significant progress in the DeFi field, providing powerful support for developers to build decentralized and permissionless applications.

  1. Oasis Network

Project Background: Oasis Network is a privacy enabled blockchain platform designed for open finance and responsible data economy.

Technical features: Oasis Network combines blockchain and privacy computing technology, aiming to protect user privacy and data security. It supports multiple privacy computing models, including TEE and zero knowledge proofs, to provide efficient and secure privacy computing solutions.

Application scenario: Oasis Network is committed to building infrastructure for the era of open finance and responsible data economy, providing technical support for data tokenization. At the same time, it also integrates ecological resources such as wallets, DeFi applications, and node operators, providing developers with rich tools and resources.

  1. Phala Network

Project background: Phala Network is a privacy computing project based on Polkadot, using TEE blockchain fusion architecture to implement secure smart contracts.

Technical features: Phala Network provides confidential computing and data protection services through TEE technology, supporting multiple trusted execution environment protocols such as Intel SGX, ARM TrustZone, AMD SEV, etc. It is committed to becoming a parallel chain for Polkadot privacy computing, providing privacy protection technology infrastructure for the entire Polkadot ecosystem.

Application scenarios: The goal of Phala Network is to provide confidential computing and data protection services for enterprises and users, supporting various privacy computing application scenarios such as financial data analysis, medical data sharing, etc.

  1. OpenMined

Project background: OpenMined is an open community dedicated to developing and promoting privacy preserving artificial intelligence (AI) technology. Its goal is to provide more reliable and secure data for AI technology by allowing individuals to maintain ownership and control over their own data.

Technical features: OpenMined mainly uses federated learning and differential privacy techniques to achieve privacy machine learning. Federated learning allows multiple participants to jointly train a model without leaking data; Differential privacy protects individual privacy by introducing noise into the data. In addition, OpenMined also utilizes encryption and blockchain technology to protect data privacy and security.

Application scenarios: OpenMined’s technology is suitable for various AI application scenarios that require privacy protection, such as medical data analysis, financial data analysis, etc. Its core project PySyft is a distributed machine learning framework for privacy protection, allowing data owners to keep their data encrypted and controlled, and share data with others for collaborative modeling.

  1. Machine learning model market combining Algorithmia and blockchain

Project background: Algorithmia project implements an interactive machine learning model marketplace through blockchain technology, allowing developers to trade and deploy machine learning models on the platform.

Technical features: Algorithmia utilizes smart contracts to implement the transaction and verification process of the model, ensuring transparency and security of transactions. At the same time, it provides an easy-to-use platform that enables developers to easily publish, discover, and deploy machine learning models.

Application scenario: Algorithmia’s marketplace provides developers, data scientists, and enterprises with an efficient channel for trading and deploying machine learning models, promoting the popularization and application of machine learning technology.