title: ÐÐ¾Ð´Ð´ÐµÑÐ¶ÐºÐ° Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ð¹ Ð¿ÑÐ¸ Ð²ÑÐ±Ð¾ÑÐµ Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Decision support when choosing a data anonymization method
Creators: name: Ð¢ÐµÑÐµÑÐµÐ½ÐºÐ¾, ÐÐ»ÐµÐ½Ð°
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: 2024
Subjects: Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
k-Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ñ
l-ÑÐ°Ð·Ð½Ð¾Ð¾Ð±ÑÐ°Ð·Ð¸Ðµ
t-Ð±Ð»Ð¸Ð·Ð¾ÑÑÑ
Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ
data anonymization
k-anonymization
l-diversity
t-closeness
differential confidentiality

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ Ð·Ð°ÐºÐ»ÑÑÐ°ÐµÑÑÑ Ð² Ð¿Ð¾Ð´Ð´ÐµÑÐ¶ÐºÐµ Ð¿ÑÐ¸Ð½ÑÑÐ¸Ñ ÑÐµÑÐµÐ½Ð¸Ñ Ð¾ Ð²ÑÐ±Ð¾ÑÐµ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ Ð·Ð°Ð´Ð°Ð½Ð½ÑÑ Ð¾Ð³ÑÐ°Ð½Ð¸ÑÐµÐ½Ð¸Ð¹. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð¸Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¸Ñ Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾ÑÑÐ¸ Ð¸ ÐºÑÐ¸ÑÐµÑÐ¸Ð¸. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: 1. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð·Ð°Ð´Ð°ÑÑ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ. 2. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð± Ð²ÑÐ±Ð¾ÑÐ° Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸. 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°ÑÑ Ð¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°ÑÑ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð´Ð°Ð½Ð½ÑÑ. ÐÑÐ»Ð¸ Ð²ÑÑÐ²Ð»ÐµÐ½Ñ ÐºÑÐ¸ÑÐµÑÐ¸Ð¸, Ð¿Ð¾ ÐºÐ¾ÑÐ¾ÑÑÐ¼ Ð¼Ð¾Ð¶Ð½Ð¾ ÑÑÐ°Ð²Ð½Ð¸Ð²Ð°ÑÑ Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ Ð²ÑÐ±Ð¸ÑÐ°ÑÑ Ð¾Ð¿ÑÐ¸Ð¼Ð°Ð»ÑÐ½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´, Ð² Ð·Ð°Ð²Ð¸ÑÐ¸Ð¼Ð¾ÑÑÐ¸ Ð¾Ñ ÑÐµÐ»ÐµÐ¹ Ð¸ Ð¿ÑÐ¸Ð¾ÑÐ¸ÑÐµÑÐ¾Ð². Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð²ÑÐ±Ð¾ÑÐ° Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð½Ð¾Ð½Ð¸Ð¼Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ Ñ Ð³ÑÐ°ÑÐ¸ÑÐµÑÐºÐ¸Ð¼ Ð¸Ð½ÑÐµÑÑÐµÐ¹ÑÐ¾Ð¼ Ð½Ð° ÑÐ·ÑÐºÐµ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ Python Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÑÐµÐ¹Ð¼Ð²Ð¾ÑÐºÐ° Flet.
description: The purpose of the work is to support decision-making on choosing the optimal anonymization method depending on the specified restrictions. The subject of the study is anonymization methods, algorithms for their implementation, as well as their features and criteria. Tasks to be solved during the research: 1. Analyze the task and methods of data anonymization. 2. To develop a way to choose the optimal method of anonymization. 3. Develop and test a software prototype. In the course of the work, methods of data anonymization were investigated. Criteria have been identified by which anonymization methods can be compared and the best method can be chosen, depending on goals and priorities. As a result of the work, an algorithm for choosing the anonymization method was developed and a software prototype with a graphical interface was implemented.