title: ÐÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ Ð´Ð°Ð½Ð½ÑÑ Ð¿ÑÐ¸ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸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: Data confidentiality in federated learning
Creators: name: ÐÑÐ±Ð°ÑÐ°ÐºÐ·ÑÐ½Ð¾Ð²Ð°, 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: 2023
Subjects: ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð°Ñ Ð¿ÑÐ¸Ð²Ð°ÑÐ½Ð¾ÑÑÑ
ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ Ð´Ð°Ð½Ð½ÑÑ
ÑÐ°ÑÑÐ½ÑÐµ Ð´Ð°Ð½Ð½ÑÐµ
Ð¼Ð°Ð»Ð¾Ð¼Ð°ÑÑÑÐ°Ð±Ð½ÑÐµ ÑÐ¸ÑÑÐµÐ¼Ñ
federated learning
differential privacy
data privacy
private data
small-scale systems

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð°ÑÐ°Ðº Ð½Ð° ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð½Ð¸Ñ. ÐÑÐµÐ´Ð¼ÐµÑÐ¾Ð¼ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ²Ð»ÑÑÑÑÑ Ð¼ÐµÑÐ¾Ð´Ñ Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð¼ Ð¾Ð±ÑÑÐµÐ½Ð¸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 ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. 2. ÐÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°ÑÑ Ð°ÑÐ°ÐºÐ¸ Ð½Ð° ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÑ ÑÐ¸ÑÑÐµÐ¼ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¼ÐµÑÐ¾Ð´Ñ Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð½Ð¸Ñ. 3. Ð ÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°ÑÑ ÑÐ¿Ð¾ÑÐ¾Ð± Ð¾Ð±ÐµÑÐ¿ÐµÑÐµÐ½Ð¸Ñ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ Ð² B2B ÑÐµÑÐµÐ½Ð¸ÑÑ. 4. Ð­ÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð¾ Ð¾ÑÐµÐ½Ð¸ÑÑ Ð³ÑÐ°Ð½Ð¸ÑÑ Ð¿ÑÐ¸Ð¼ÐµÐ½Ð¸Ð¼Ð¾ÑÑÐ¸ ÑÐºÐ°Ð·Ð°Ð½Ð½Ð¾Ð³Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð°. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¸ Ð±ÐµÐ·Ð¾Ð¿Ð°ÑÐ½Ð¾ÑÑÑ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÑÐ»Ð¸ Ð¿ÑÐ¾Ð°Ð½Ð°Ð»Ð¸Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ñ ÑÐ¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² Ð¾Ð±Ð»Ð°ÑÑÐ¸ Ð·Ð°ÑÐ¸ÑÑ ÐºÐ¾Ð½ÑÐ¸Ð´ÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾ÑÑÐ¸ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÐ¹ Ð¿ÑÐ¾ÑÐ¾ÑÐ¸Ð¿ ÑÐ¸ÑÑÐµÐ¼Ñ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ñ Ð´Ð¸ÑÑÐµÑÐµÐ½ÑÐ¸Ð°Ð»ÑÐ½Ð¾Ð¹ Ð¿ÑÐ¸Ð²Ð°ÑÐ½Ð¾ÑÑÑÑ. ÐÑÐ» ÑÐ´ÐµÐ»Ð°Ð½ Ð²ÑÐ²Ð¾Ð´, ÑÑÐ¾ Ð¿ÑÐµÐ´Ð»Ð°Ð³Ð°ÐµÐ¼ÑÐ¹ ÑÐ¿Ð¾ÑÐ¾Ð± Ð¿Ð¾Ð´ÑÐ¾Ð´Ð¸Ñ Ð´Ð»Ñ ÑÐµÐ´ÐµÑÐ°ÑÐ¸Ð²Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð² ÑÐ¸ÑÑÐµÐ¼Ð°Ñ B2B. ÐÐ¾Ð»ÑÑÐµÐ½Ð½ÑÐµ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¼Ð¾Ð³ÑÑ Ð±ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ñ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸ÑÐ¼Ð¸ Ð¿ÑÐ¸ ÑÐ¾Ð·Ð´Ð°Ð½Ð¸Ð¸ ÑÐ¾Ð²Ð¼ÐµÑÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ.
description: The purpose of the work is to study attacks on the confidentiality of federated learning systems and protection against them. The subject of the research is the methods of ensuring confidentiality in federal learning. The research set the following goals: 1. Analysis of federated learning technologies. 2. Analysis of attacks on confidentiality of federated learning systems and methods of protection against them. 3. Implementation of a way to ensure privacy in B2B solutions. 4. Experimental evaluation of the limits of applicability of the specified method. In the course of the work, the technologies and security of federated learning were investigated. Modern research in the field of protecting the confidentiality of federated learning was analyzed. As a result of the work, a software prototype of a federated learning system with differential privacy was developed. It was concluded that the proposed method is suitable for federated learning in small-scale business-to-business systems. The results obtained can be used by organizations when creating collaborative machine learning models.