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Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning

Mina Rafiei, Mohammadmahdi Maheri, Hamid R. Rabiee

TL;DR

The paper investigates privacy risks in Model-Agnostic Meta-Learning (MAML) under a distributed setting where task-learners share gradients with a meta-learner. It demonstrates that shared gradients can encode information about both the task-specific support and query data, enabling two passive membership inference attacks on these data components. It introduces four noise-injection strategies and evaluates them against MNIST and Fashion-MNIST in a few-shot MAML setup, showing that carefully chosen noise can reduce attack success while preserving learning performance. Overall, the work provides a first in-depth analysis of gradient-based privacy leakage in MAML, offers practical defense mechanisms, and highlights architecture-dependent leakage characteristics that warrant further robustness research.

Abstract

Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally learn models from sensitive data, termed support sets. These task learners subsequently share model-related information, such as gradients or loss values, which is computed using another part of the data termed query set, with a meta-learner. The meta-learner employs this information to update its meta-knowledge. Despite the absence of explicit data sharing, privacy concerns persist. This paper examines potential data leakage in a prominent metalearning algorithm, specifically Model-Agnostic Meta-Learning (MAML). In MAML, gradients are shared between the metalearner and task-learners. The primary objective is to scrutinize the gradient and the information it encompasses about the task dataset. Subsequently, we endeavor to propose membership inference attacks targeting the task dataset containing support and query sets. Finally, we explore various noise injection methods designed to safeguard the privacy of task data and thwart potential attacks. Experimental results demonstrate the effectiveness of these attacks on MAML and the efficacy of proper noise injection methods in countering them.

Privacy Challenges in Meta-Learning: An Investigation on Model-Agnostic Meta-Learning

TL;DR

The paper investigates privacy risks in Model-Agnostic Meta-Learning (MAML) under a distributed setting where task-learners share gradients with a meta-learner. It demonstrates that shared gradients can encode information about both the task-specific support and query data, enabling two passive membership inference attacks on these data components. It introduces four noise-injection strategies and evaluates them against MNIST and Fashion-MNIST in a few-shot MAML setup, showing that carefully chosen noise can reduce attack success while preserving learning performance. Overall, the work provides a first in-depth analysis of gradient-based privacy leakage in MAML, offers practical defense mechanisms, and highlights architecture-dependent leakage characteristics that warrant further robustness research.

Abstract

Meta-learning involves multiple learners, each dedicated to specific tasks, collaborating in a data-constrained setting. In current meta-learning methods, task learners locally learn models from sensitive data, termed support sets. These task learners subsequently share model-related information, such as gradients or loss values, which is computed using another part of the data termed query set, with a meta-learner. The meta-learner employs this information to update its meta-knowledge. Despite the absence of explicit data sharing, privacy concerns persist. This paper examines potential data leakage in a prominent metalearning algorithm, specifically Model-Agnostic Meta-Learning (MAML). In MAML, gradients are shared between the metalearner and task-learners. The primary objective is to scrutinize the gradient and the information it encompasses about the task dataset. Subsequently, we endeavor to propose membership inference attacks targeting the task dataset containing support and query sets. Finally, we explore various noise injection methods designed to safeguard the privacy of task data and thwart potential attacks. Experimental results demonstrate the effectiveness of these attacks on MAML and the efficacy of proper noise injection methods in countering them.
Paper Structure (23 sections, 1 theorem, 13 equations, 12 figures, 4 tables)

This paper contains 23 sections, 1 theorem, 13 equations, 12 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Privacy in Machine Learning
  4. Privacy in Meta-Learning
  5. Meta-Learning & Avenues for Privacy Leakage
  6. Meta-Learning Overview
  7. Further Details about MAML Algorithm
  8. Privacy Leakage in MAML
  9. Attack Methodology
  10. Threat Model
  11. Intuition
  12. Membership Inference Attack on Query Data
  13. Membership Inference Attack on Support Data
  14. How to protect MAML from Attacks?
  15. Experiments
  16. ...and 8 more sections

Key Result

Proposition 3.1

Consider a neural network architecture featuring a biased fully-connected layer, preceded by a (possibly unbiased) fully-connected layer. This network undergoes training using the MAML algorithm. Let's focus on a particular task, denoted as $i$, associated with data $D = \{ D^s, D^q \}$, consisting

Figures (12)

  • Figure 1: "The Federated Meta-Learning Process: The tasks' data, highlighted in gray, are not directly shared with the meta-learner, but there may be data leakage in the shared gradient.
  • Figure 2: The Query Reconstruction Process Utilizing the Target Image as Prior Subsequently, the reconstructed query and its prior are compared to infer the membership of the target image in the task query set.
  • Figure 3: The Support Reconstruction Process Utilizing the Target Image as Prior: Subsequently, the reconstructed support and its prior are compared to infer the membership of the target image in the task support set.
  • Figure 4: Attack sample runs. The right image in each part represents the output, and the left image is the attack input. As observed, when the input is a member, the output is similar to it. However, when it is not a member, the attack iterations cause changes, making it similar to the shape of the original member image. Thus, we can differentiate between these two by comparing the input and output classes.
  • Figure 5: Investigating Attack Accuracy Across Data Ways: Surprisingly, the accuracy of the attack remains relatively stable despite variations in the number of data ways. This suggests that the attack performance is not significantly influenced by changes in the number of classes (ways), indicating a consistent level of accuracy across different class configurations.
  • ...and 7 more figures

Theorems & Definitions (1)

  • Proposition 3.1