提出隐私保护个性化学习的分类框架并揭示多任务与元学习的差异
Privacy in Metalearning and Multitask Learning: Modeling and Separations
- 构建隐私个性化学习的分类体系，涵盖不同学习框架与攻击者威胁模型
- 首次证明私有多任务学习与私人元学习之间存在本质差异
- 为隐私敏感场景下的个性化模型选择提供理论依据
模型个性化使每个面临不同学习任务的个体能够训练出比单独学习更精准的模型。这一目标在多任务学习和元学习等形式化框架中得到体现。然而，合并数据进行个性化学习会带来隐私风险，因为个体模型的输出可能依赖于其他人的数据。本文系统研究了差分隐私下的个性化学习。首要贡献是构建了一个形式化框架的分类体系，涵盖不同的学习范式与攻击者威胁模型。第二项主要贡献是证明了不同选择下的个性化学习问题之间存在分离性，特别是首次揭示了私有多任务学习与私人元学习之间的本质区别。
原文摘要 · Abstract (English)
Model personalization allows a set of individuals, each facing a different learning task, to train models that are more accurate for each person than those they could develop individually. The goals of personalization are captured in a variety of formal frameworks, such as multitask learning and metalearning. Combining data for model personalization poses risks for privacy because the output of an individual's model can depend on the data of other individuals. In this work we undertake a systematic study of differentially private personalized learning. Our first main contribution is to construct a taxonomy of formal frameworks for private personalized learning. This taxonomy captures different formal frameworks for learning as well as different threat models for the attacker. Our second main contribution is to prove separations between the personalized learning problems corresponding to different choices. In particular, we prove a novel separation between private multitask learning and private metalearning.
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