Differentially Private Fractional Frequency Moments Estimation with Polylogarithmic SpaceDownload PDF

29 Sept 2021, 00:31 (modified: 13 Mar 2022, 01:27)ICLR 2022 PosterReaders: Everyone
Keywords: Differential Privacy, Fractional Frequency Moments
Abstract: We prove that $\mathbb{F}_p$ sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when $p\in(0, 1]$. $\mathbb{F}_p$ sketch uses only polylogarithmic space, exponentially better than existing DP baselines and only worse than the optimal non-private baseline by a logarithmic factor. The evaluation shows that $\mathbb{F}_p$ sketch can achieve reasonable accuracy with strong privacy guarantees. The code for evaluation is included in the supplementary material.
One-sentence Summary: We prove that $\mathbb{F}_p$ sketch, a well-celebrated streaming algorithm for frequency moments estimation, is differentially private as is when $p\in(0, 1]$.
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