鲁棒洗牌模型中无分布奇偶学习的样本复杂性
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model
摘要：
在差分隐私的洗牌模型中，我们给出了可实现的情况下无分布奇偶学习的样本复杂度的下界。也就是说，我们证明了学习$d$位奇偶校验函数的样本复杂度为$Ω(2^{d/2})$ .我们的结果扩展了最近的一个类似的下界的私人不可知学习奇偶函数的洗牌模型的样本复杂性的Cheu和Ullman。我们还勾画了一个简单的洗牌模型协议，证明我们的结果是紧到$poly(d)$因素。
英文摘要：
We provide a lowerbound on the sample complexity of distribution-free parity learning in the realizable case in the shuffle model of differential privacy. Namely, we show that the sample complexity of learning $d$-bit parity functions is $Ω(2^{d/2})$. Our result extends a recent similar lowerbound on the sample complexity of private agnostic learning of parity functions in the shuffle model by Cheu and Ullman. We also sketch a simple shuffle model protocol demonstrating that our results are tight up to $poly(d)$ factors.
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