Automatic depersonalization of confidential information

Abstract

Objectives. As the scope of personal data transmitted online continues to grow, national legislatures are increasingly regulating the storage and processing of digital information. This paper raises the problem of protecting personal data and other confidential information such as bank secrecy or medical confidentiality of individuals. One approach to the protection of confidential data is to depersonalize it, i.e., to transform it so that it becomes impossible to identify the specific subject to whom the data belongs. The aim of the work is to develop a method for the rapid and safe automation of the depersonalization process using machine learning technologies.Methods. The authors propose the use of artificial intelligence models to implement a system for the automatic depersonalization of personal data without the use of human labor to preclude the possibility of recognizing confidential information even in unstructured data with sufficient accuracy. Rule-based algorithms for improving the precision of the depersonalization system are described.Results. In order to solve this problem, a model of named entity recognition is trained on confidential data provided by the authors. In conjunction with rule-based algorithms, an F1 score greater than 0.9 is achieved. For solving specific depersonalization problems, a choice between several implemented anonymization algorithm variants can be made.Conclusions. The developed system solves the problem of automatic anonymization of confidential data. This opens an opportunity to ensure the secure processing and transmission of confidential information in many areas, such as banking, government administration, and advertising campaigns. The automation of the depersonalization process makes it possible to transfer confidential information in cases where it is necessary, but not currently possible due to legal restrictions. The distinctive feature of the developed solution is that both structured data and unstructured data are depersonalized, including the preservation of context.