Data has become an indispensable element of economic activities and a new generation of production factors after land, energy, population, and food. However, the massive data managed by enterprises cannot be exchanged and collaboratively calculated with the data held by other enterprises, resulting in a large amount of data that cannot generate value.
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In the information age, data has become a new generation of production factors
In economics, production factors, also known as production inputs, are the basic resources that people use to produce goods and services. In the epoch-making book "Principles of Economics" by the famous British economist Marshall, he put forward the four elements of production theory - land, labor, capital and entrepreneurial ability, that is, national income (NI) is the reward of the four elements , that is, national income (NI) = labor wages (w) + land rent (r) + capital interest (i) + operating profit (π). This "four-in-one formula" summarizes the center of production theory and distribution theory in Western economics, and has been generally accepted by people for more than a century.
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Figure: Factors of Production in Different Historical Stages
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Privacy brings data dilemma, MPC realizes data collaborative computing
Nowadays, human beings have already extended their social activities to cyberspace, and continuously contribute data to cyberspace in daily life. A large amount of data is collected, calculated, analyzed, and excavated even beyond the original data magnitude. . However, due to the plaintext property of the data, once the data is granted to others, the owner loses the ownership of the data. Therefore, due to the privacy protection of data, the massive data managed by enterprises cannot be exchanged and collaboratively calculated with the data held by other enterprises, resulting in a large amount of data that cannot generate value.
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Private Computing Expands Huge Business Prospects for the Digital World
Bitcoin pioneered the combination of cryptocurrency and peer-to-peer payment system, opening the door to decentralization. The introduction of smart contract functions in Ethereum has greatly improved the scalability of the blockchain, and various applications can be deployed on the blockchain. It is based on these characteristics that early public blockchain networks such as Bitcoin and Ethereum have developed and grown, attracting a large number of blockchain and cryptography enthusiasts around the world. Many traditional institutions have also continued to enter the blockchain field and continue to explore decentralization. various possibilities.
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The "operator" of blockchain data - PlatON
one,
one,Build a wider credit investigation networktwo,
two,Supply Chain Financial Infrastructurethree,
three,Joint Data Analysis of Research Institutions. Based on the private AI computing network, the basic joint data analysis platform enables research institutions to conduct secure multi-party calculations with another institution without leaving the local data, and obtain analysis and calculation results that meet the needs of scientific research.
Four,
Four,Reliable traceability of high-end outsourcing product quality. Traditional outsourced product quality management and traceability are highly dependent on systems, from design, components and raw materials, processes, equipment, systems, and the environment. The quality requirements of high-end industries for outsourced products must be foolproof. The products must be strictly implemented and implemented according to the established design and process, and all links must be conscientious and responsible, so that qualified products can be produced without any omissions.
The product full-cycle traceability platform based on the privacy AI computing network, through all links from design input, component materials, production links, experimental data, acceptance conclusions, etc., all data is uploaded to the chain to ensure that the full-cycle information of product development cannot be tampered with and Traceable, so as to solve the problem of product credibility and quality traceability. The use of these data is then protected through privacy calculations to ensure that the privacy and security of the data is guaranteed when the relevant accumulated data is analyzed in big data.
In order to create a public infrastructure in the all-digital era, PlatON continues to optimize technology and iterate the underlying infrastructure. At present, using the native Token transfer performance test method and EOS under the same test conditions for comparative testing, PlatON has achieved comprehensive performance in a virtual environment Leading the way, we will continue to focus on the data field and accelerate the construction of the data market.
