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Sublinear Space Private Algorithms Under the Sliding Window Model

Jalaj Upadhyay

Pacific Ballroom #174

Keywords: [ Privacy-preserving Statistics and Machine Learning ]

Abstract: The Differential privacy overview of Apple states, ``Apple retains the collected data for a maximum of three months." Analysis of recent data is formalized by the {\em sliding window model}. This begs the question: what is the price of privacy in the sliding window model? In this paper, we study heavy hitters in the sliding window model with window size $w$. Previous works of Chan et al. (2012) estimates heavy hitters with an error of order $\theta w$ for a constant $\theta >0$. In this paper, we give an efficient differentially private algorithm to estimate heavy hitters in the sliding window model with $\widetilde O(w^{3/4})$ additive error and using $\widetilde O(\sqrt{w})$ space.

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