Poster
Sublinear Space Private Algorithms Under the Sliding Window Model
Jalaj Upadhyay

Tue Jun 11th 06:30 -- 09:00 PM @ Pacific Ballroom #174

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.

Author Information

Jalaj Upadhyay (Johns Hopkins University)

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