Three years ago, the AI ​​network PlatON published its project whitepaper on data protection for data protection. After more than three years of direct market presence, PlatON published Whitepaper 2.0 on social media on September 14th, which is an oath to use private AI network technology.

PlatON 2.0 has formally defined the three phase goals, namely the construction of a decentralized private computer network, an AI market (artificial intelligence) and an AI collaboration network based on the three main technologies of blockchain, data protection and AI.

PlatON2.0 Layering and further development of the data protection AI network

The PlatON White Paper 2.0 reveals in detail the design architecture of its common chain, which uses a three-layer network architecture, namely the consensus layer, the data protection computer network and the AI ​​network.

Level 1: Consensus Network

The consensus network is a P2P (peer-to-peer) blockchain network made up of nodes. The nodes of the blockchain are connected via the P2P protocol. The consensus protocol can be negotiated without outside trust, which can create a certain feeling of serverless.

In the field of intelligent traffic, for example, AI is the “brain” behind driverless vehicles. These autonomous vehicles need to trust each other so that they can work together to achieve their common goals. The AI ​​system has no mechanism to ensure that these autonomous vehicles reach consensus in a credible way. Therefore, they need a trustworthy third party or a consensus network to support the subjects in the AI ​​system in fulfilling their tasks. However, various data leaks have shown that third parties can expose the public to security and privacy issues.

Certainly the blockchain network cannot solve all problems. The disadvantages are low efficiency and data transparency.

  1. We can execute smart contracts on the blockchain. However, due to the limitations in performance and transaction costs, the blockchain cannot support smart contracts to perform calculation logic that is too complex, and we can only access the data stored in the chain. Besides, the data is also limited. The AI ​​model training cannot only be completed on Layer 1.
  2. On the blockchain, every participant receives a full copy of the data and all transaction data is open and transparent. Obviously, the native blockchain technology doesn’t have the ability to protect privacy. A data protection computing protocol based on homomorphic encryption, zero knowledge proof, TEE and other technologies must be overlaid on the consensus network so that it can protect the confidentiality of data and computing in the chain.

Layer2: Data Protection Computing Network

The data protection computing network above the consensus network layer is the most important layer of PlatON. This layer includes data protection algorithms and data storage protocols in order to build an open market for trading in computing power for AI networks and the future, which provides essential data processing functions for PlatON and PlatON-based applications. The data protection computer network enables users to contribute computing power and connects users who have computing needs. In particular, it performs data calculations for users through the data protection calculation and provides incentives for computing service providers through PlatON-native tokens.

The data on the data protection computer network is generally stored locally, and the security and privacy of the data are ensured by technologies such as MPC (Secure Multiparty Computing) and federated learning. It makes the data available and invisible, so the subjects prefer to share sensitive data (such as consumption and health information). Over time, the market will accumulate more and more high quality data. AI professionals are motivated to create and share better AI models.

Layer3: collaborative AI network

Compared to the more robust combination between Layer1 and Layer2, Layer3 is a relatively independent network. The ultimate goal of this layer is to form an autonomous AI network. AI models can be trained using the data sets and computer resources in the data protection computer network. We can provide AI models in the AI ​​network and create an AI service market through externally acting AI agents. Multi-agent systems and other technologies allow us to operate AI agents to communicate, collaborate, and create more innovative AI services.

The Fetch project, for example, has committed itself to the development of AEA (Autonomous Economic Agents) and their organized cooperation. AEA are software entities that can perform actions without external stimuli. You can intelligently search for and interact with other autonomous economic actors.

Many projects are currently trying to combine blockchain, privacy computing and AI. Some combine data protection computing and blockchain to improve the data protection and computational capabilities of the blockchain, and some combine blockchain and AI to create an AI market. Some use the decentralized blockchain to build computing power and data markets. However, they can only meet part of the need for data protection AI. Only a few projects like Fetch, SingularityNET and PlatON are dedicated to building an AI ecosystem.

Among other things, the aim of PlatON is to build a data protection computer network and an AI collaboration network. The main applications are AI training and services, as well as autonomous agents.

Based on much more complex development work and the three-layer network architecture design, PlatON offers the advantages of decentralization, security, data protection, efficiency, flexibility, etc. It can be used for more flexible and extensive application scenarios such as finance, medicine, smart city, and IoT.

Financial Applications

With private computing technology, operators, internet platforms, insurance companies and other multi-channel data will open up more control-like private data to strengthen collaboration with banks. The confidential nature will help the integration and the business. The banks will implement the entire process monitoring of pre-credit, incredit and pro-credit in order to improve the timeliness of the control. The intersection of data protection and the common data protection query makes it easy to determine the extensive credit risk of the customer without revealing a customer ID and private information. That forms common insurance, prevention and control.

Pharmaceutical Applications

As an AI infrastructure, PlatON offers a reliable collaboration environment for hospitals, pharmaceutical companies and various scientific research institutions. It combines activities, research fields, modes of operation and data flows from different fields to achieve a large-scale data aggregation effect so that we can get the most out of pharmaceutical datasets such as clinical trials, drugs, electrical health records and patient genomic data, etc. PlatON will support discovery and Accelerate the R&D process of new drugs if a data analysis and mining system of various technical levels such as pharmacogenomics, disease isomics, collateral pharmacology, ice structure simulation, etc. is set up.

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