title: ÐÐ°ÑÐ¸ÑÐ° Ð¾Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹
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: Protection from attacks of artificial neural network research
Creators: name: ÐÑÑÐ¾Ð¼Ð¸Ð½Ð°, ÐÐ½Ð°ÑÑÐ°ÑÐ¸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: 2024
Subjects: ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
ÐÐ½ÑÐ¾ÑÐ¼Ð°ÑÐ¸Ñ
Ð°ÑÐ°ÐºÐ¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ
Ð²ÑÐ²Ð¾Ð´ ÑÐ»ÐµÐ½ÑÑÐ²Ð°
Ð¸Ð½Ð²ÐµÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸
research attacks
membership inference
model inversion

description: Ð¦ÐµÐ»ÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð·Ð°ÑÐ¸ÑÑ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹ Ð¾Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð¿Ð° Â«Ð¸Ð½Ð²ÐµÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸Â» Ð¸ Â«Ð²ÑÐ²Ð¾Ð´ ÑÐ»ÐµÐ½ÑÑÐ²Ð°Â». 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Ð½Ð°Ð»Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½ÑÑ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. 2. ÐÐ½Ð°Ð»Ð¸Ð· Ð¸ ÑÐ¸ÑÑÐµÐ¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð¿Ð° Â«Ð²ÑÐ²Ð¾Ð´ ÑÐ»ÐµÐ½ÑÑÐ²Ð°Â» Ð¸ Â«Ð¸Ð½Ð²ÐµÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸Â». 3. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð¼ÐµÑÐ¾Ð´Ð° Ð·Ð°ÑÐ¸ÑÑ Ð¾Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ ÑÐ¸Ð¿Ð° Â«Ð²ÑÐ²Ð¾Ð´ ÑÐ»ÐµÐ½ÑÑÐ²Ð°Â» Ð¸ Â«Ð¸Ð½Ð²ÐµÑÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»Ð¸Â», ÑÐºÑÐ¿ÐµÑÐ¸Ð¼ÐµÐ½ÑÐ°Ð»ÑÐ½Ð°Ñ Ð¾ÑÐµÐ½ÐºÐ° ÐµÐ³Ð¾ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐ¸ Ð·Ð°ÑÐ¸ÑÐµ Ð²ÑÑÐ¸ÑÐ»Ð¸ÑÐµÐ»ÑÐ½ÑÑ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÑ ÑÐµÑÐµÐ¹. Ð ÑÐ¾Ð´Ðµ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ñ Ð²Ð¸Ð´Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° ÑÐ¸ÑÑÐµÐ¼Ñ Ð¸ÑÐºÑÑÑÑÐ²ÐµÐ½Ð½Ð¾Ð³Ð¾ Ð¸Ð½ÑÐµÐ»Ð»ÐµÐºÑÐ°, Ð¼ÐµÑÐ¾Ð´Ñ Ð·Ð°ÑÐ¸Ñ, ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½ ÑÐ½Ð¸Ð²ÐµÑÑÐ°Ð»ÑÐ½ÑÐ¹ Ð¼ÐµÑÐ¾Ð´ Ð¸ ÑÐ¼Ð¾Ð´ÐµÐ»Ð¸ÑÐ¾Ð²Ð°Ð½Ð° Ð°ÑÐ°ÐºÐ° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ. 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 Ð² Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐ¸Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸ÑÑ Ð¸ Ð¼Ð¾Ð´Ð¸ÑÐ¸ÐºÐ°ÑÐ¸ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð·Ð°ÑÐ¸Ñ Ð¾Ñ Ð°ÑÐ°Ðº Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ.
description: The aim of the work is to develop a universal method of protecting artificial neural networks from research attacks such as "model inversion" and "membership inference". Tasks to be solved during the research: 1. Analysis of methods for the implementation of artificial neural network re-search attacks. 2. Analysis and systematization of methods of protection against attacks of re-search such as "withdrawal of membership" and "inversion of the model". 3. Development of a universal method of protection against attacks of the research of the "membership withdrawal" and "model inversion" types, experimental evaluation of its effectiveness in protecting computational models of neural networks. In the course of the work, the types of research attacks on artificial intelligence systems, protection methods were investigated, a universal method was implemented, and a research attack was modeled. The results obtained can be used in further implementations and modifications of methods of protection against research attacks.