NeurIPS 2018spotlight116 citations
Local Differential Privacy for Evolving Data
Matthew Joseph, Aaron Roth, Jonathan Ullman, Bo Waggoner
Abstract
There are now several large scale deployments of differential privacy used to collect statistical information about users. However, these deployments periodically recollect the data and recompute the statistics using algorithms designed for a single use. As a result, these systems do not provide meaningful privacy guarantees over long time scales. Moreover, existing techniques to mitigate this effect do not apply in the ``local model'' of differential privacy that these systems use.
BibTeX
@inproceedings{NEURIPS2018_a0161022,
author = {Joseph, Matthew and Roth, Aaron and Ullman, Jonathan and Waggoner, Bo},
booktitle = {Advances in Neural Information Processing Systems},
editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
pages = {},
publisher = {Curran Associates, Inc.},
title = {Local Differential Privacy for Evolving Data},
url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/a01610228fe998f515a72dd730294d87-Paper.pdf},
volume = {31},
year = {2018}
}