title: ÐÐ½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐµÐºÑÐµÑ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: Analysis of privacy-preserving machine learning methods based on secret sharing
Creators: name: Ð¢ÐµÑÑÑÐºÐ¸Ð½Ð°, ÐÐ»Ð¸Ð·Ð°Ð²ÐµÑÐ°
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: 2025
Subjects: ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÑÑ ÐµÐ¼Ñ ÑÐ°Ð·Ð´ÐµÐ»ÐµÐ½Ð¸Ñ ÑÐµÐºÑÐµÑÐ°
Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½ÑÐµ Ð¼Ð½Ð¾Ð³Ð¾ÑÑÐ¾ÑÐ¾Ð½Ð½Ð¸Ðµ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ
privacy-preserving machine learning
secret sharing
secure multi-party computation

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description: The purpose of the study is to choose optimal hyperparameters of privacy-preserving machine learning tools for classification of network traffic anomalies and estimate the tools vulnerability to attacks on machine learning systems. The subject of the work is security of privacy-preserving machine learning tools. The research set the following goals: 1. Comparing privacy-preserving machine learning tools based on secret sharing and choosing the most suitable ones for further work. 2. Experimentally chose the optimal hyperparameters of privacy-preserving machine learning tools for classification of network traffic anomalies. 3. Analysis of the tools vulnerability to attacks on machine learning systems. During the work privacy-preserving machine learning tools based on secret sharing were analyzed. As a result, the tools were used to classify network traffic anomalies. A theoretical analysis of the tools vulnerability to attacks on machine learning systems was carried out. Based on the trained models, experiments were conducted showing the vulnerability of the tools to some attacks on machine learning systems.