High-performance data risk influence aspect quantitative evaluation method based on deduplication algorithm

Abstract

The invention discloses a high-performance data risk influence aspect quantitative evaluation method based on a deduplication algorithm, particularly relates to the field of data security. A data preprocessing module, a data caching module, a data risk module, a data value module and a data updating module are included. According to application of the big data deduplication algorithm based on loglogcounting, linear counting and adaptive probabilitycounting in a data security risk influence aspect evaluation scene, a high-performance data security risk influence aspect calculation method is carried out on massive subjects, and a high-performance probability counting algorithm is adopted to realize low-cost and high-performance data security risk influence aspect measurement; a weighted deduplication quantity statistical method of different data types is adopted to quantitatively evaluate the data security risk influence aspect, the method can be applied to various scenes of data security behavior risk measurement, and more comprehensive, efficient and objective quantitative evaluation of risks is realized.

Description

technical field [0001] The present invention relates to the technical field of data security, and more specifically, the present invention relates to a quantitative evaluation method for high-performance data risk impact surface based on a deduplication algorithm. Background technique [0002] In the field of data security, the measurement of risk depends on several core elements, including the possibility of risk occurrence and the impact of risk. The characterization of the impact of risk requires an assessment of the scope and importance of the affected data. If the amount of data affected is very large, it has a high potential risk level. This kind of risk assessment and measurement is widely used in pre-event data vulnerability risk assessment, real-time data behavior monitoring, offline behavior risk assessment, and post-event data leakage impact scenarios. [0003] For the subject being evaluated, whether it is the user, IP, data interface, or data vulnerability dime...

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Problems solved by technology

[0004] In order to overcome the above-mentioned defects of the prior art, the embodiment of the present invention provides a high-performance data risk impact surface quantitative evaluation method based on a deduplication algorithm. In the evaluation process, it is necessary to invest a large amount of computing resources to measure the impact surface, and improve the performance of the data impact surface calculation method

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