Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning

Syed Mhamudul Hasan, Abdur R. Shahid, Ahmed Imteaj

TL;DR

This paper introduces the Robustness Carbon Trade-off Index (RCTI), a novel metric that captures the sensitivity of carbon emissions to changes in adversarial robustness, and demonstrates the RCTI through an experiment involving evasion attacks.

Abstract

The widespread adoption of machine learning (ML) across various industries has raised sustainability concerns due to its substantial energy usage and carbon emissions. This issue becomes more pressing in adversarial ML, which focuses on enhancing model security against different network-based attacks. Implementing defenses in ML systems often necessitates additional computational resources and network security measures, exacerbating their environmental impacts. In this paper, we pioneer the first investigation into adversarial ML's carbon footprint, providing empirical evidence connecting greater model robustness to higher emissions. Addressing the critical need to quantify this trade-off, we introduce the Robustness Carbon Trade-off Index (RCTI). This novel metric, inspired by economic elasticity principles, captures the sensitivity of carbon emissions to changes in adversarial robustness. We demonstrate the RCTI through an experiment involving evasion attacks, analyzing the interplay between robustness against attacks, performance, and carbon emissions.

Towards Sustainable SecureML: Quantifying Carbon Footprint of Adversarial Machine Learning

TL;DR

This paper introduces the Robustness Carbon Trade-off Index (RCTI), a novel metric that captures the sensitivity of carbon emissions to changes in adversarial robustness, and demonstrates the RCTI through an experiment involving evasion attacks.

Abstract

The widespread adoption of machine learning (ML) across various industries has raised sustainability concerns due to its substantial energy usage and carbon emissions. This issue becomes more pressing in adversarial ML, which focuses on enhancing model security against different network-based attacks. Implementing defenses in ML systems often necessitates additional computational resources and network security measures, exacerbating their environmental impacts. In this paper, we pioneer the first investigation into adversarial ML's carbon footprint, providing empirical evidence connecting greater model robustness to higher emissions. Addressing the critical need to quantify this trade-off, we introduce the Robustness Carbon Trade-off Index (RCTI). This novel metric, inspired by economic elasticity principles, captures the sensitivity of carbon emissions to changes in adversarial robustness. We demonstrate the RCTI through an experiment involving evasion attacks, analyzing the interplay between robustness against attacks, performance, and carbon emissions.
Paper Structure (18 sections, 4 equations, 5 figures, 3 tables, 1 algorithm)

This paper contains 18 sections, 4 equations, 5 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Sustainable and Green ML
  4. Adversarial Attack on ML
  5. Methodology: Measuring Robustness- Carbon Trade-off Index of Adversarial ML
  6. Design Goal
  7. An Economic Approach to Quantify The Robustness and Carbon Emission Trade-Off
  8. Experiment and Analysis
  9. Experimental Setup
  10. Environment and Dataset
  11. Baseline and Adversarial Model Training
  12. Analysis of Energy, Accuracy, Time Consumption, and Emissions Under Attacks
  13. Energy consumption
  14. Accuracy
  15. Time Consumption
  16. ...and 3 more sections

Figures (5)

  • Figure 1: Sustainable SecureML: (Adversarial ML landscape) (a) An ML model lifecycle when dealing with data sources hosted on untrusted networks, (b) a classification of the adversarial attacks related to the ML lifecycle phases, (c) various defense mechanisms to defend against adversarial attacks, (d) an illustration of the attacks and their defenses, demonstrating the dynamic interplay of attack and defense tactics in untrusted network settings (figure credit: ART Toolboxart2018), and (e) the scope of our work: the intersection of ML, cybersecurity, and environmental sustainability.
  • Figure 2: MNIST baseline and adversarial classifier model architecture
  • Figure 3: Robustness of adversarial models under $\epsilon$-based different evasion attacks with (left) Fast Gradient (FG) and (right) Projected Gradient Descent (PGD). For PGD, $\Delta\mathcal{R}( \epsilon)$ was set to 0 in case of $\epsilon = 0.4$ and $0.5$ for visualization purpose. Their original values were $\infty$ as shown in table \ref{['tbl:rcti-attacks']}
  • Figure 4: Carbon emission ($\Delta\mathcal{C}$) under $\epsilon$-based different evasion attacks with (left) Fast Gradient (FG) and (right) Projected Gradient Descent (PGD)
  • Figure 5: RCTI (Robustness-Carbon Trade-off Index) of (left) Fast Gradient (FG) and (right) Projected Gradient Descent (PGD) under different $\epsilon$. For PGD, $RCTI$ was set to 0 in case of $\epsilon = 0.4$ and $0.5$ for visualization purpose. Their original values were $\infty$ as shown in table \ref{['tbl:rcti-attacks']}.