Differentially Private Stream Processing at Scale
Abstract
We design, to the best of our knowledge, the first differentially private (DP) stream aggregation processing system at scale. Our system -- Differential Privacy SQL Pipelines (DP-SQLP) -- is built using a streaming framework similar to Spark streaming, and is built on top of the Spanner database and the F1 query engine from Google. Towards designing DP-SQLP we make both algorithmic and systemic advances, namely, we (i) design a novel (user-level) DP key selection algorithm that can operate on an unbounded set of possible keys, and can scale to one billion keys that users have contributed, (ii) design a preemptive execution scheme for DP key selection that avoids enumerating all the keys at each triggering time, and (iii) use algorithmic techniques from DP continual observation to release a continual DP histogram of user contributions to different keys over the stream length. We empirically demonstrate the efficacy by obtaining at least reduction in error over meaningful baselines we consider. We implemented a streaming differentially private user impressions for Google Shopping with DP-SQLP. The streaming DP algorithms are further applied to Google Trends.
Keywords
Cite
@article{arxiv.2303.18086,
title = {Differentially Private Stream Processing at Scale},
author = {Bing Zhang and Vadym Doroshenko and Peter Kairouz and Thomas Steinke and Abhradeep Thakurta and Ziyin Ma and Eidan Cohen and Himani Apte and Jodi Spacek},
journal= {arXiv preprint arXiv:2303.18086},
year = {2024}
}