论文标题
差异私人彩票机制
The Differentially Private Lottery Ticket Mechanism
论文作者
论文摘要
我们提出了差异化私人彩票机制（DPLTM）。基于彩票票证假设的端到端差异私人培训范式。 DPLTM使用“高质量的获胜者”（通过我们的自定义得分功能）选择，可显着改善与最先进的私密性权衡。我们表明，DPLTM收敛速度更快，可以随着隐私预算消费减少而尽早停止。我们进一步表明，来自DPLTM的门票可在数据集，域和体系结构之间转移。我们对几个公共数据集的广泛评估为我们的主张提供了证据。
We propose the differentially private lottery ticket mechanism (DPLTM). An end-to-end differentially private training paradigm based on the lottery ticket hypothesis. Using "high-quality winners", selected via our custom score function, DPLTM significantly improves the privacy-utility trade-off over the state-of-the-art. We show that DPLTM converges faster, allowing for early stopping with reduced privacy budget consumption. We further show that the tickets from DPLTM are transferable across datasets, domains, and architectures. Our extensive evaluation on several public datasets provides evidence to our claims.
