A Note on Sanitizing Streams with Differential Privacy

11/26/2021

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by   Haim Kaplan, et al.

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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.