Medical data joint learning system and method based on trusted computing and privacy protection

A technology of medical data and learning methods, applied in the field of safe sharing of medical big data, can solve the problems of incompleteness, singleness, medical data privacy protection and scattered data mining, etc., to ensure security and avoid privacy leakage.

Active Publication Date: 2019-09-03
上海锘崴信息科技有限公司
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  • Abstract
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Problems solved by technology

[0006] The invention relates to a medical data joint learning system and method based on trusted computing and privacy protection, and provides a complete set of service systems based on medical big data security sharing, trusted computing, deep mining, authority authentication, and multi-platform joint learning. It solves the fragmented, single and incomplete problems of medical data privacy protection and data mining at this stage

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  • Medical data joint learning system and method based on trusted computing and privacy protection
  • Medical data joint learning system and method based on trusted computing and privacy protection
  • Medical data joint learning system and method based on trusted computing and privacy protection

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Embodiment Construction

[0050] The principles, features, and system flow of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not used to limit the scope of the present invention.

[0051] Such as figure 1 As shown, the medical data joint learning scheme based on trusted computing and privacy protection includes three parts:

[0052] First, Data Contributor Management;

[0053] The local management layer realizes the localized registration, storage and calculation of all original medical data by data contributors (such as hospitals, medical research institutions and other medical big data owners). Specifically, all raw data of data contributors are completely registered and stored locally (within the firewall). At the same time, all calculations involving raw data are limited to local isolation. This design fundamentally avoids the leakage of private data.

[0054] The local manag...

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Abstract

The invention relates to a medical data joint learning system and method based on trusted computing and privacy protection. A joint learning center control layer receives non-sensitive meta-information uploaded by a data contributor through a data contributor management layer of a data node where the joint learning center control layer is located for filing, and original data is locally registered, stored and subjected to isolation calculation; and the joint learning center control layer processes a joint learning request initiated by the data miner through the data miner interaction layer, summarizes insensitive intermediate results obtained by performing local isolation calculation on the data nodes based on the original data in a safe calculation area, and returns a final joint learningresult to the data miner interaction layer. The invention provides a whole set of service system based on medical big data security sharing, trusted computing, deep mining, authority authentication and multi-platform joint learning, and solves the problems of scattered, single and incomplete medical data privacy protection and data mining at the present stage.

Description

technical field [0001] The invention relates to safe sharing of medical big data, credible mining and privacy security protection. Specifically, it refers to a medical big data joint learning system and method based on trusted computing and privacy protection. Background technique [0002] The existing medical big data search, sharing, and data mining services are still in the immature stage, lacking in-depth and credible mining of data, authority certification, and systematic standards and protection measures have not yet been formed. Strict laws and lack of protection systems and standards have caused a large number of medical data owners such as hospitals and medical research institutions to be unwilling or afraid to share their data resources, which has seriously affected the rapid progress and development of medical disciplines under the trend of Internet big data. Development, for example, comprehensive diagnosis and analysis of diseases, big data statistical analysis...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62G16H50/70
CPCG06F21/6245G16H50/70
Inventor 王爽郑灏王晓峰汤海旭窦佐超王文浩
Owner 上海锘崴信息科技有限公司
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