Sublinear Space Private Algorithms Under the Sliding Window Model
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 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.
Cite
Text
Upadhyay. "Sublinear Space Private Algorithms Under the Sliding Window Model." International Conference on Machine Learning, 2019.Markdown
[Upadhyay. "Sublinear Space Private Algorithms Under the Sliding Window Model." International Conference on Machine Learning, 2019.](https://mlanthology.org/icml/2019/upadhyay2019icml-sublinear/)BibTeX
@inproceedings{upadhyay2019icml-sublinear,
title = {{Sublinear Space Private Algorithms Under the Sliding Window Model}},
author = {Upadhyay, Jalaj},
booktitle = {International Conference on Machine Learning},
year = {2019},
pages = {6363-6372},
volume = {97},
url = {https://mlanthology.org/icml/2019/upadhyay2019icml-sublinear/}
}