Sensitive data detection method and device based on machine learning

Abstract

The invention discloses a sensitive data detection method and device based on machine learning. When data is synchronized from a production environment to a development test environment, sensitive fields must be desensitized. According to a machine learning principle, a data table field sensitivity identification problem is converted into a text classification problem, and an NLP technology is applied to train a model to identify sensitive fields. The method is combined with a conventional detection means, the recognition effect is continuously improved through a self-learning algorithm, the risk that sensitive fields are omitted to the external environment can be reduced, manual intervention is reduced, and the working efficiency is improved.

Description

technical field [0001] The invention belongs to the field of computer systems, and in particular relates to a machine learning-based sensitive data detection method and device. Background technique [0002] The banking industry is an area with very strict data security requirements. Sensitive fields must be desensitized before all data is exported. [0003] Sensitive fields are generally desensitized through scripts submitted by developers. But in the face of huge data tables and the number of fields, developers may not be able to cover all sensitive fields. A traditional detection method is through regular expression matching. Regular expressions are a rule-based matching technique, limited by specific rules. For example, it has a strong ability to identify fields with strong regularity such as mobile phone number and card number. For less regular content, such as work units and home addresses, the recognition ability is weak. Data fetching positions often involve a la...

AI Technical Summary

Problems solved by technology

Therefore, it is easy to cause incomplete detection

If manual observation is performed, the efficiency of the retrieval process will be very low

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