Understanding Data Importance in Machine Learning Attacks: Does Valuable Data Pose Greater Harm?

TL;DR

This paper investigates how the importance of data samples in machine learning affects their vulnerability to various attacks, revealing that valuable data can be more susceptible and suggesting improved defense strategies.

Contribution

It introduces an analysis of the link between data importance and attack vulnerability, proposing sample-specific metrics to enhance membership inference attacks.

Findings

High importance data samples are more vulnerable to certain attacks

Sample-specific criteria can improve membership inference performance

Highlights the need for defenses balancing data utility and security

Abstract

Machine learning has revolutionized numerous domains, playing a crucial role in driving advancements and enabling data-centric processes. The significance of data in training models and shaping their performance cannot be overstated. Recent research has highlighted the heterogeneous impact of individual data samples, particularly the presence of valuable data that significantly contributes to the utility and effectiveness of machine learning models. However, a critical question remains unanswered: are these valuable data samples more vulnerable to machine learning attacks? In this work, we investigate the relationship between data importance and machine learning attacks by analyzing five distinct attack types. Our findings reveal notable insights. For example, we observe that high importance data samples exhibit increased vulnerability in certain attacks, such as membership inference and model stealing. By analyzing the linkage between membership inference vulnerability and data importance, we demonstrate that sample characteristics can be integrated into membership metrics by introducing sample-specific criteria, therefore enhancing the membership inference performance. These findings emphasize the urgent need for innovative defense mechanisms that strike a balance between maximizing utility and safeguarding valuable data against potential exploitation.