Research on Intelligent Perception Algorithm for Sensitive Information

Author:

Huo Lin1,Jiang Juncong2ORCID

Affiliation:

1. International College, Guangxi University, Nanning 530004, China

2. School of Computer and Electronic Information, Guangxi University, Nanning 530004, China

Abstract

In the big data era, a tremendous volume of electronic documents is transmitted via the network, many of which include sensitive information about the country and businesses. There is a pressing need to be able to perform intelligent sensing of sensitive information on these documents in order to be able to discover and guarantee the security of sensitive information in this enormous volume of documents. Although the low effectiveness of manual detection is resolved by the current method of handling sensitive information, there are still downsides, such as poor processing effects and slow speed. This study creatively proposes the Text Sensitive Information Intelligent Perception algorithm (TSIIP), which detects sensitive words at the word level and sensitive statements at the statement level to obtain the final assessment score of the text. We experimentally compare this algorithm with other methods on an existing dataset of sensitive Chinese information. We use the metrics measuring the accuracy of the binary classification model, where the F1 score reaches 0.938 (+0.6%), and the F2 score reaches 0.946 (+1%), and the experimental results fully demonstrate the superiority of this algorithm.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

Fluid Flow and Transfer Processes,Computer Science Applications,Process Chemistry and Technology,General Engineering,Instrumentation,General Materials Science

Reference32 articles.

1. Seger, C. (2018). An Investigation of Categorical Variable Encoding Techniques in Machine Learning: Binary versus One-Hot And Feature Hashing. [Independent Thesis Basic Level (Degree of Bachelor), Royal Institute of Technology].

2. A vector space model for automatic indexing;Salton;Commun. ACM,1975

3. Cavnar, W.B., and Trenkle, J.M. (1994, January 26–28). N-gram-based text categorization. Proceedings of the SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval, Las Vegas, NV, USA.

4. An information-theoretic perspective of tf–idf measures;Aizawa;Inf. Process. Manag.,2003

5. Gradient-based learning applied to document recognition;LeCun;Proc. IEEE,1998

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