Data desensitization method and system based on power grid data acquisition

A data collection and data desensitization technology, applied in the fields of electrical digital data processing, digital data protection, natural language data processing, etc., can solve the problem of data desensitization that cannot meet the needs of multi-dimensional and multiple data types, data leakage, cracking, etc. question

Inactive Publication Date: 2021-08-20
WUHAN ZHONGYUAN ELECTRONICS INFORMATION
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

With the continuous improvement of power grid intelligence and measurement accuracy, the traditional regular matching rules that rely on expert domain knowledge to formulate can no longer meet the data desensitization needs of multiple dimensions and data types
And if the desensitization method is fixed, it is also easy to be cracked by high computing power, resulting in data leakage

Method used

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  • Data desensitization method and system based on power grid data acquisition
  • Data desensitization method and system based on power grid data acquisition
  • Data desensitization method and system based on power grid data acquisition

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Embodiment 2

[0047] refer to Figure 4 , the second aspect of the present invention provides a data desensitization system 1 based on power grid data collection, including an acquisition module 11, a fitting module 12, a generation module 13 and a return module 14, the acquisition module 11 is used to acquire Multidimensional power data and identifying sensitive data therein; the fitting module 12 is used to draw a frequency histogram according to the frequency of each data item of each sensitive data, and fit the first distribution curve according to it; the generating module 13. It is used to use the trained generative confrontational neural network to generate a second distribution curve whose distribution distance from the first distribution curve is lower than a threshold; the return module 14 is used to generate a second distribution curve according to the second distribution curve and Lap Lass noise, sensitive data in external requests that return multidimensional data.

[0048] Fu...

Embodiment 3

[0050] A third aspect of the present invention provides an electronic device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are used by the one or more The processor executes, so that the one or more processors implement the data desensitization method based on grid data collection provided by the first aspect of the present invention.

[0051] refer to Figure 4 , the electronic device 500 may include a processing device (such as a central processing unit, a graphics processing unit, etc.) 501, which may be loaded into a random access memory (RAM) 503 according to a program stored in a read-only memory (ROM) 502 or loaded from a storage device 508 Various appropriate actions and processing are performed by the programs in the program. In the RAM 503, various programs and data necessary for the operation of the electronic device 500 are also stored. The processing device 501 , ROM 502 and RAM 503 are conne...

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Abstract

The invention relates to a data desensitization method and system based on power grid data acquisition. The method comprises the following steps: acquiring multi-dimensional power data and identifying sensitive data in the multi-dimensional power data; drawing a frequency histogram according to the frequency of each data item of each sensitive data, and fitting a first distribution curve according to the frequency histogram; using the trained generative adversarial neural network to generate a second distribution curve, wherein the distribution distance between the second distribution curve and the first distribution curve is less than a threshold value; and returning sensitive data in an external request of the multi-dimensional data according to the second distribution curve and Laplacian noise. According to the method, approximate distribution of sensitive data is dynamically generated through the generative adversarial neural network, desensitization of data with different sensitivities is realized in combination with Laplacian noise, and the requirements of different data application scenes are met.

Description

technical field [0001] The invention belongs to the field of electric power data processing, and in particular relates to a data desensitization method and system based on grid data collection. Background technique [0002] At present, the big data platform built within the State Grid stores a large amount of sensitive data such as power marketing data, power dispatching data, and personal power consumption information. These data involve personal privacy and company secrets, and there is no effective processing mechanism in the data generation, transmission, storage, processing, and use. There are hidden dangers of privacy leakage, leakage of user privacy information, and leakage of sensitive data within the State Grid. , directly causing double losses to the State Grid’s reputation and economy. [0003] On the other hand, a large amount of power data needs to be mined and analyzed. It is undoubtedly a waste of the big data platform to block and hide the data too much. How...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/62G06F40/216G06N3/04
CPCG06F21/6245G06F40/216G06N3/045
Inventor 吴天音陈恩泽向路萍陈君
Owner WUHAN ZHONGYUAN ELECTRONICS INFORMATION
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