Multi-party data sharing method based on generative adversarial network and related equipment
A technology for data sharing and synthesizing data, applied in biological neural network models, neural learning methods, electrical digital data processing, etc., can solve problems such as poor utility of shared data, difficult generalization ability of generated models, limited number of data records, etc., to achieve high utility effect
Pending Publication Date: 2022-05-06
BEIJING UNIV OF POSTS & TELECOMM
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Problems solved by technology
However, due to the limited number of data records in each partial dataset, it is difficult for the learned generative model to maintain good generalization ability, resulting in poor utility of the final shared data.
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[0053]In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.
[0054] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application shall have the usual meanings understood by those skilled in the art to which the present application belongs. "First", "second" and similar words used in the embodiments of the present application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "connected" are not l...
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The invention provides a multi-party data sharing method based on a generative adversarial network and related equipment, and the method comprises the steps: transmitting random vectors to a generator network one by one for processing, and obtaining synthetic data corresponding to each random vector; sending the synthetic data corresponding to all the random vectors to each discriminator network for discrimination to obtain a discrimination result of each discriminator network, and calculating an average acceptance probability of the synthetic data corresponding to each random vector according to the discrimination result of each discriminator network; and comparing a preset probability with the average acceptance probability of the synthetic data corresponding to each random vector, and when the average acceptance probability of the synthetic data corresponding to a certain random vector is greater than or equal to the preset probability, taking the synthetic data corresponding to the random vector as shared data. By means of the method, the finally shared data can have high effectiveness.
Description
technical field [0001] This application relates to data sharing technology, in particular to a multi-party data sharing method and related equipment based on generative confrontation networks. Background technique [0002] The privacy-preserving multi-party data sharing technology provides a feasible solution to the privacy leakage problem caused by multi-party data sharing. The Differential Privacy (DP) technology proposed in recent years provides a feasible solution to the privacy leakage problem caused by data sharing. Different from traditional anonymity-based privacy models, differential privacy provides a strict, quantifiable privacy protection method for sensitive data. By adding an appropriate amount of noise to the statistical results to ensure that modifying a record in the data set will not have a significant impact on the statistical results, so as to achieve the requirement of privacy protection. [0003] Multi-party data sharing can be achieved by each party ...
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Login to View More IPC IPC(8): G06F16/176G06N3/04G06N3/08G06K9/62G06F17/18
CPCG06F16/176G06N3/082G06F17/18G06N3/048G06N3/045G06F18/256
Inventor 苏森程祥王振亚梁锦涛杨皓程
Owner BEIJING UNIV OF POSTS & TELECOMM