AI Technology for Safer Data Analysis Across Multiple Organizations

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A new AI technology has been developed that will allow for safer integrated analysis of data held by multiple organizations. The technology, called non-readily identifiable data collaboration analysis, will enable the sharing of abstract data that cannot be easily identified with the original data, allowing for the integration of personal information held by different institutions, such as companies, hospitals, and local governments.

To achieve this, the research team introduced a framework for the mathematical definitions of readily identifiable data and proposed an integrated analysis algorithm that shares only the abstracted data that cannot be readily identified with the original data. By doing so, more data can be used in analysis involving personal information.

This technology has a range of potential applications, including predicting diseases through the integrated analysis of test and medication data from multiple medical institutions and enhancing educational effectiveness through integrated analysis of student data from various educational institutions. The technology is expected to facilitate the development of a new platform for gathering high-quality personal information from various organizations while protecting the original data and using AI for comprehensive data analysis.

Collecting sufficient data without distribution bias is essential to improve the accuracy of AI analysis. Moreover, it is essential for AI technology to collect data dispersed across multiple institutions while keeping certain information confidential, such as personal information and know-how. The use of data is often restricted if personal information is involved and identifiable in the shared data.

The team behind the development of this technology believes that it will significantly improve the accuracy of AI analysis, particularly in cases where personal information is involved. By enabling the integration of personal information held by multiple parties without compromising their privacy, the technology will enable institutions to work together to achieve more accurate results.

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The research paper detailing the technology has been published in the journal Information Fusion. Overall, this new AI technology is set to revolutionize the way that data is analyzed and shared across organizations, paving the way for more comprehensive and accurate analysis of personal information.

Frequently Asked Questions (FAQs) Related to the Above News

What is the new AI technology developed for safer data analysis across multiple organizations?

The new AI technology is called non-readily identifiable data collaboration analysis, which enables the sharing of abstract data that cannot be easily identified with the original data.

What does the framework introduced by the research team define?

The framework introduced by the research team defines the mathematical definitions of readily identifiable data and proposed an integrated analysis algorithm that shares only the abstracted data that cannot be readily identified with the original data.

What are the potential applications of this technology?

The potential applications of this technology include predicting diseases through the integrated analysis of test and medication data from multiple medical institutions and enhancing educational effectiveness through integrated analysis of student data from various educational institutions.

How will this technology improve the accuracy of AI analysis?

This technology will significantly improve the accuracy of AI analysis, especially in cases where personal information is involved. By enabling the integration of personal information held by multiple parties without compromising their privacy, the technology will enable institutions to work together to achieve more accurate results.

Where has the research paper detailing the technology been published?

The research paper detailing the technology has been published in the journal Information Fusion.

Please note that the FAQs provided on this page are based on the news article published. While we strive to provide accurate and up-to-date information, it is always recommended to consult relevant authorities or professionals before making any decisions or taking action based on the FAQs or the news article.

Jai Shah
Jai Shah
Meet Jai, our knowledgeable writer and manager for the AI Technology category. With a keen eye for emerging AI trends and technological advancements, Jai explores the intersection of AI with various industries. His articles delve into the practical applications, challenges, and future potential of AI, providing valuable insights to our readers.

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