中国个人敏感信息检测新框架

IF 3.2 4区计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Connection Science Pub Date : 2024-01-03 DOI:10.1080/09540091.2023.2298310

Chenglong Ren, Xiao Lan, Xingshu Chen, Yonggang Luo, Shuhua Ruan

引用次数: 0

摘要

随着社交网络的快速发展，个人敏感信息泄露造成的危害日益严重。为了及时发现和识别个人敏感信息，我们需要对社交网络中的个人敏感 ...

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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A novel framework for Chinese personal sensitive information detection

With the rapid development of social networks, the harm caused by the leakage of personal sensitive information is becoming increasingly serious. In order to detect and identify personal sensitive ...

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来源期刊

Connection Science 工程技术-计算机：理论方法

CiteScore

6.50

自引率

39.60%

发文量

审稿时长

3 months

期刊介绍： Connection Science is an interdisciplinary journal dedicated to exploring the convergence of the analytic and synthetic sciences, including neuroscience, computational modelling, artificial intelligence, machine learning, deep learning, Database, Big Data, quantum computing, Blockchain, Zero-Knowledge, Internet of Things, Cybersecurity, and parallel and distributed computing. A strong focus is on the articles arising from connectionist, probabilistic, dynamical, or evolutionary approaches in aspects of Computer Science, applied applications, and systems-level computational subjects that seek to understand models in science and engineering.