Sublinear Space Private Algorithms Under the Sliding Window Model

Jalaj Upadhyay
Proceedings of the 36th International Conference on Machine Learning, PMLR 97:6363-6372, 2019.

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 this Paper

BibTeX
@InProceedings{pmlr-v97-upadhyay19a, title = {Sublinear Space Private Algorithms Under the Sliding Window Model}, author = {Upadhyay, Jalaj}, booktitle = {Proceedings of the 36th International Conference on Machine Learning}, pages = {6363--6372}, year = {2019}, editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan}, volume = {97}, series = {Proceedings of Machine Learning Research}, month = {09--15 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v97/upadhyay19a/upadhyay19a.pdf}, url = {https://proceedings.mlr.press/v97/upadhyay19a.html}, 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.} }
Endnote
%0 Conference Paper %T Sublinear Space Private Algorithms Under the Sliding Window Model %A Jalaj Upadhyay %B Proceedings of the 36th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2019 %E Kamalika Chaudhuri %E Ruslan Salakhutdinov %F pmlr-v97-upadhyay19a %I PMLR %P 6363--6372 %U https://proceedings.mlr.press/v97/upadhyay19a.html %V 97 %X 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.
APA
Upadhyay, J.. (2019). Sublinear Space Private Algorithms Under the Sliding Window Model. Proceedings of the 36th International Conference on Machine Learning, in Proceedings of Machine Learning Research 97:6363-6372 Available from https://proceedings.mlr.press/v97/upadhyay19a.html.

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