Separating Local & Shuffled Differential Privacy via Histograms
Cryptography and Security
2020-04-15 v4
Abstract
Recent work in differential privacy has highlighted the shuffled model as a promising avenue to compute accurate statistics while keeping raw data in users' hands. We present a protocol in this model that estimates histograms with error independent of the domain size. This implies an arbitrarily large gap in sample complexity between the shuffled and local models. On the other hand, the models are equivalent when we impose the constraints of pure differential privacy and single-message randomizers.
Cite
@article{arxiv.1911.06879,
title = {Separating Local & Shuffled Differential Privacy via Histograms},
author = {Victor Balcer and Albert Cheu},
journal= {arXiv preprint arXiv:1911.06879},
year = {2020}
}
Comments
14 pages, two tables. Accepted to ITC 2020