Orchard: Differentially Private Analytics at Scale

Authors: 

Edo Roth, Hengchu Zhang, Andreas Haeberlen, and Benjamin C. Pierce, University of Pennsylvania

Abstract: 

This paper presents Orchard, a system that can answer queries about sensitive data that is held by millions of user devices, with strong differential privacy guarantees. Orchard combines high accuracy with good scalability, and it uses only a single untrusted party to facilitate the query. Moreover, whereas previous solutions that shared these properties were custom-built for specific queries, Orchard is general and can accept a wide range of queries. Orchard accomplishes this by rewriting queries into a distributed protocol that can be executed efficiently at scale, using cryptographic primitives.

Our prototype of Orchard can execute 14 out of 17 queries chosen from the literature; to our knowledge, no other system can handle more than one of them in this setting. And the costs are moderate: each user device typically needs only a few megabytes of traffic and a few minutes of computation time. Orchard also includes a novel defense against malicious users who attempt to distort the results of a query.

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BibTeX
@inproceedings {258981,
author = {Edo Roth and Hengchu Zhang and Andreas Haeberlen and Benjamin C. Pierce},
title = {Orchard: Differentially Private Analytics at Scale},
booktitle = {14th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 20)},
year = {2020},
isbn = {978-1-939133-19-9},
pages = {1065--1081},
url = {https://www.usenix.org/conference/osdi20/presentation/roth},
publisher = {{USENIX} Association},
month = nov,
}
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