title: ÐÐ±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ðµ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° ÑÐ¿ÐµÑÐ¸Ð°Ð»Ð¸ÑÑÐ°
titleType: Subtitle
title: Ensuring the confidentiality of the relational presentation of medical data using anonymization
Creators: name: ÐÐ°Ð½, ÐÐ°ÑÐ°Ð»ÑÑ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2022
Subjects: Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
Ð°ÑÐ°ÐºÐ¸ Ð¿Ð¾Ð²ÑÐ¾ÑÐ½Ð¾Ð¹ Ð¸Ð´ÐµÐ½ÑÑÐ¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¸
Ð¼ÐµÐ´Ð¸ÑÐ¸Ð½ÑÐºÐ¸Ðµ Ð´Ð°Ð½Ð½ÑÐµ
Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ Ð¿ÑÐ¸Ð²Ð°ÑÐ½Ð¾ÑÑÑ
anonymization
re-identification attacks
medical data
differential privacy

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description: The topic of the final qualification work: "Ensuring the confidentiality of the relational presentation of medical data using anonymization".The purpose of this work is to ensure the confidentiality of medical data by protecting against re-identification attacks. The subject of the study is methods of protection against attacks on the relational representation of medical data.Tasks to be solved in the course of the study:Analyze privacy attacks and methods for identifying anonymized medical data.Analysis of methods of protection against re-identification attacks.Implementation of an anonymization algorithm to protect against re-identification attacks and experimental testing of the received anonymized medical data.In the course of the work, the main attacks of re-identification aimed at medical data were investigated. Existing methods of protection were analyzed.As a result of the work, an anonymization method was developed, the effectiveness of the proposed methods was demonstrated. It was concluded that the data obtained provided the necessary level of anonymization without significant loss of their usefulness.The results obtained can be used to anonymize the data before transmitting it to machine learning systems.