Thoughts on data privacy, AI and blockchain technology
When the popularization of computers began 70 years ago, people could not imagine having a second life in a digital world. Virtual avatars continue to push the boundaries of identity and improve living conditions in the online world where individuals leave their mark in the form of data.
According to Statista, connected devices worldwide are expected to reach 30.9 billion units by 2025. Connected devices and services generate a substantial share of data; the International Data Corporation predicts that by 2025, global data will measure 163 zettabytes, with one zettabyte equaling 1 trillion gigabytes. That’s ten times the 16.1 zettabytes of data generated in 2016.
How to access and use data effectively? The answer is artificial intelligence (AI).
Sixty years of AI development
During a six-month seminar at Dartmouth College in the summer of 1956, a group of young scientists, including Marvin Minsky, coined the term “artificial intelligence“.
Today, most are familiar with AI; it has become integrated into the daily routine. The convenience and advancement of AI are everywhere, from online shopping to industrial production.
Deloitte Touche Tohmatsu’s 2019 white paper on global AI development projected that the global AI market would surpass $6 trillion by 2025 at a compound growth rate of 30% from 2017 to 2025. A report research published by PricewaterhouseCoopers suggests that global gross domestic product will be 14% higher by 2030 due to the adoption of AI, bringing an additional $15.7 trillion to the global economy, more than the current production of China and India combined.
The field of AI has flourished over the past 60 years. However, with the advent of the fourth industrial revolution – the technological revolution – the upper limit of AI has become increasingly apparent.
For AI to become both the variable and the central technology of the ongoing technological reform, three key elements are essential: data, algorithms and computing power.
The first challenge is the pressure of data governance and privacy. In 2018, the European Union introduced the General Data Protection Regulation, and in 2021 China’s Data Security Law and Personal Information Protection Law came into effect. The latter focuses on the rights and interests of Chinese citizens in terms of privacy, dignity and property. The law defines personal information as anything identified or identifiable about someone electronically recorded or otherwise documented. The strengthened rules on privacy and personal data aimed to effectively prevent the misuse of data.
How should AI break down these barriers and advance in the face of the challenges of data privacy, high costs, and technological centralization?
AI for all
Blockchain and privacy-preserving computation inspired AI.
Blockchain’s consensus algorithms help accomplish collaborative tasks between subjects in AI systems, and its technical features enable the assetization of data. This can incentivize adding more comprehensive data ranges, algorithms, and computing power to build better AI models.
Recently, a product launched by a company showed users and is marketing a new universal AI application.
The PlatON Privacy Preserving Computing Network – a tentative name – is a decentralized computing infrastructure network for data sharing and privacy preservation. It was designed to integrate the three elements of AI – computing power, algorithms and data – into a user product. Anyone can be a data owner, user, algorithm developer or developer to access the platform and perform various tasks. Such a decentralized way of collecting the data, algorithms, and computing power needed for computation creates a safe new model of AI.
As a commercially available product, Plato at the service of businesses and individuals. For example: as data owners, individuals and institutions can add data as nodes and participate in computing tasks published on the platform. This innovative approach enables ownership identification, pricing, data protection and assetization, all while respecting confidentiality.
Individuals and organizations can contribute computing power to the platform to perform the computing tasks of others. Idle computing power becomes publicly available for computing tasks that earn rewards.
As algorithm providers, AI developers can develop algorithms, and computing tasks performed with their algorithms will generate revenue. This shapes a free, open and sustainable AI market.
PlatON has followed several privacy and data preservation measures: collaborative computing with secure multi-party computing, zero-knowledge proof, homomorphic encryption, verifiable computing, federal learning, and other cryptographic technologies to protect local data. Computational results such as trained AI models are also leak-proof, and products can efficiently execute smart contracts and run popular deep learning frameworks for versatility, compatibility, and high availability.
The product is currently in closed beta testing, as such a large and complex platform will inevitably face challenges:
- How can multiple parties evaluate the data?
- How can data be accurately captured and applied as it flows between different parties?
- How to incentivize AI developers to provide basic algorithms?
However, this is a data complex never seen before. The integration and application of new technologies takes time, and PlatON has taken a step forward in the commercialization of data.
To learn more about this project, visit our website.
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