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.

使用 MAML 算法作为案例，研究了元学习中潜在的数据泄漏问题，并提出了针对任务数据的成员推断攻击，以及用于保护任务数据隐私和防止攻击的噪声注入方法。实验证明了这些攻击对 MAML 的有效性以及适当的噪声注入方法在对抗这些攻击中的功效。