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Application of Computer Simulation to the Anonymization of Personal Data: State-of-the-Art and Key Points

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Abstract

The first part of the study of the automated processing of personal data for the purpose of their anonymization and analysis is presented. This part is an overview and aims to analyze the state of research in this area and systematize the available results. Results of the analysis of a wide range of issues of anonymization are presented, which formed a systematic understanding of the state of research and substantiated the choice of direction for further study. First, definitions of the main terms and concepts used in connection with anonymization of personal data are formulated, including association with the legislation of the Russian Federation. The directions of research are grouped into four sections: anonymization methods, implementation issues, applications of anonymized data processing, and deanonymization issues. For each group of anonymization methods—randomization, grouping, data distribution and application control— descriptions of the main algorithms are given and their advantages and disadvantages are analyzed. Implementation issues concern such concepts as the usefulness of anonymized data, limitations of the applicability of universal algorithms, and the reliability in relation to maintaining the anonymity of personal data subjects. Among applications that have formed the demand for the processing anonymized data, medical, biological, and genetic research and law enforcement are discussed. In the final part, the most interesting facts of deanonymization are mentioned and a short review of mass media is given.

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ACKNOWLEDGMENTS

This work was carried out using the infrastructure of the shared computer center “High-Performance Computations and Big Data” of the Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences.

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    A. V. Borisov, A. V. Bosov & A. V. Ivanov

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Borisov, A.V., Bosov, A.V. & Ivanov, A.V. Application of Computer Simulation to the Anonymization of Personal Data: State-of-the-Art and Key Points. Program Comput Soft 49, 232–246 (2023). https://doi.org/10.1134/S0361768823040047

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