We study a class of private learning problems in which the data is a join of
private and public features. This is often the case in private personalization
tasks such as recommendation or ad prediction, in which features related to
individuals are sensitive, while features related to items (the movies or songs
to be recommended, or the ads to be shown to users) are publicly available and
do not require protection. A natural question is whether private algorithms can
achieve higher utility in the presence of public features. We give a positive
answer for multi-encoder models where one of the encoders operates on public
features. We develop new algorithms that take advantage of this separation by
only protecting certain sufficient statistics (instead of adding noise to the
gradient). This method has a guaranteed utility improvement for linear
regression, and importantly, achieves the state of the art on two standard
private recommendation benchmarks, demonstrating the importance of methods that
adapt to the private-public feature separation.

研究一类私有学习问题，其中数据是私有特征和公共特征的联接。针对多编码器模型中的公共特征，开发了新的算法以仅保护某些足够统计量，从而实现了线性回归的保证效用改进，并在两个标准的私有推荐基准上达到了最先进的水平，证明了适应私有 - 公共特征分离的方法的重要性。