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

Rui Wen, Michael Backes, Yang Zhang

TL;DR

This work investigates the relationship between data importance and machine learning attacks by analyzing five distinct attack types and demonstrates that sample characteristics can be integrated into membership metrics by introducing sample-specific criteria, therefore enhancing the membership inference performance.

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.

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

TL;DR

This work investigates the relationship between data importance and machine learning attacks by analyzing five distinct attack types and demonstrates that sample characteristics can be integrated into membership metrics by introducing sample-specific criteria, therefore enhancing the membership inference performance.

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.
Paper Structure (34 sections, 6 equations, 31 figures)

This paper contains 34 sections, 6 equations, 31 figures.

Table of Contents

  1. Introduction
  2. Background
  3. Machine Learning Models
  4. Data Importance
  5. Evaluation Setup
  6. Learning Characteristic
  7. Membership Inference Attack
  8. Privacy Onion Effect
  9. Actively Modify Sample Importance
  10. Data Augmentation
  11. Model Stealing
  12. Same Distribution Query
  13. Different Distribution Query
  14. Backdoor Attack
  15. Attribute Inference Attack
  16. ...and 19 more sections

Figures (31)

  • Figure 1: Relationship between loss and importance value. Low importance samples statistically have higher losses.
  • Figure 2: Relationship between distance to the decision boundary and importance value. Low importance samples are statistically closer to the decision boundary. The distance measured with different norms can be found in \ref{['appendix_mia']}.
  • Figure 3: Log-scale ROC curve: membership inference attack based on the distance to the decision boundary. High importance samples exhibited substantially higher true-positive rates, particularly in the low false-positive rate region. Results with different norms can be found in \ref{['appendix_mia']}.
  • Figure 4: Membership advantage: membership inference attack based on three metrics. Attack advantage steadily escalates as the importance value of the samples increases.
  • Figure 5: Incorporation of importance values in calibrating membership inference metrics improves the attack performance, demonstrating the strength of employing sample-specific membership criteria.
  • ...and 26 more figures

Theorems & Definitions (1)

  • Definition 4.1: Membership Inference Security Game CCNSTT22