Differential Privacy offers strong guarantees such as immutable privacy under post processing. Thus it is often looked to as a solution to learning on scattered and isolated data. This work focuses on supervised manifold learning, a paradigm that can generate fine-tuned manifolds for a target use case. Our contributions are two fold. 1) We present a novel differentially private method \textit{PrivateMail} for supervised manifold learning, the first of its kind to our knowledge. 2) We provide a novel private geometric embedding scheme for our experimental use case. We experiment on private "content based image retrieval" - embedding and querying the nearest neighbors of images in a private manner - and show extensive privacy-utility tradeoff results, as well as the computational efficiency and practicality of our methods.

中文翻译：

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差分私有监督的流形学习以及诸如私有图像检索之类的应用

差异隐私提供了强有力的保证，例如在后期处理过程中的不变隐私。因此，它经常被视为学习分散和孤立数据的一种解决方案。这项工作着重于有监督的流形学习，这种范式可以为目标用例生成经过微调的流形。我们的贡献是双重的。1）我们提出了一种新颖的差异私有方法\ textit {PrivateMail}，用于监督流形学习，这是我们所了解的第一个方法。2）我们为实验用例提供了一种新颖的私有几何嵌入方案。我们对私有“基于内容的图像检索”进行了实验-以私有方式嵌入和查询图像的最近邻居-并显示了广泛的隐私-实用性权衡结果，以及我们方法的计算效率和实用性。