Assessment of the Digital Footprint of a Participant in Internet Interactions Using Mathematical Statistics Methods

The purpose of the study. In the modern world, everyone is an active user of resources hosted on the global network. Logging in from different devices (computers, tablets, phones), we visit many electronic pages, correspond and view content, leaving our electronic footprint in the system each time. The purpose of the study is to identify such a trace, record it and process it using mathematical statistics methods in order to obtain personalized and generalized data for various industries. Also, based on the results obtained, recommendations are expected to be formed to protect digital footprint data from possible malicious use. The paper examines the concept and describes in detail the sources of a digital trace of a participant in Internet interactions, identifies methods and tools for collecting and pre-processing information. The concept of active and passive components of a digital fingerprint is disclosed. Attention is focused on the need to classify the information contained in the digital trace as personal data, since it can sometimes determine a person more accurately than a document, which, in turn, requires legislative protection in terms of collection, storage, processing and further use. The article provides examples of using digital fingerprint information in various industries: criminalistics, marketing research, targeted advertising, etc. Materials and methods. The article discusses in sufficient detail various methods of mathematical statistics for assessing the digital footprint of a network user, and provides steps that allow for the collection, preprocessing and analysis of information contained in a digital fingerprint using the Python, R programming languages and various software tools. The use of statistical analysis methods, the use of an interaction matrix, visualization capabilities, machine learning algorithms, recommendation systems, network metrics, time series, and sensitivity analysis methods are considered. Results. Despite the fact that data obtained using mathematical statistics methods can be useful for businesses, government organizations, research institutes, and even law enforcement agencies, data collected in the course of activities on a global network and linked to a specific individual can pose a clear threat if used by intruders. These assessment methods are available both for constructive tendencies to improve our lives by selecting personalized relevant content and improving the service, and for destructive activities through manipulative actions and blackmail. In this regard, knowledge of the trace left and the possibilities of using this data in its original and processed form makes it possible to improve the quality of life, on the one hand, and protect personal information, on the other. Conclusion. Attention is drawn to the need for information control of one’s digital trace, in connection with which the article provides a number of recommendations for the implementation of safe Internet interaction.