The literature on data sanitization aims to design algorithms that take an input dataset and produce a privacy-preserving version of it, that captures some of its statistical properties. In this note we study this question from a streaming perspective and our goal is to sanitize a data stream. Specifically, we consider low-memory algorithms that operate on a data stream and produce an alternative privacy-preserving stream that captures some statistical properties of the original input stream.

翻译：关于数据净化的文献旨在设计一种算法,这种算法采用输入数据集,并产生一种保存隐私的版本,以捕捉其某些统计属性。在本说明中,我们从流的角度来研究这一问题,我们的目标是使数据流净化。具体地说,我们考虑在数据流上运作的低模算法,并产生一种替代的保存隐私的流,以捕捉原始输入流的一些统计属性。