A Differentially Private Data Analytics API at Scale

Ryan Rogers, LinkedIn

Abstract: 

We present a privacy system that leverages differential privacy to protect LinkedIn members' data while also providing audience engagement insights to enable marketing analytics related applications. We detail the differentially private algorithms and other privacy safeguards used to provide results that can be used with existing real-time data analytics platforms, specifically with the open sourced Pinot system. Our privacy system provides user-level privacy guarantees. As part of our privacy system, we include a budget management service that enforces a strict differential privacy budget on the returned results to the analyst. This budget management service brings together the latest research in differential privacy into a product to maintain utility given a fixed differential privacy budget.

Ryan Rogers, LinkedIn

Ryan Rogers is a Senior Software Engineer in the applied research group at LinkedIn where he works on designing and implementing private algorithms and systems for data analytics and machine learning. Prior to working at LinkedIn, he worked with the ML Privacy team at Apple where he was the technical lead on developing the private algorithms for the private federated learning project. He received his PhD in Applied Mathematics from the University of Pennsylvania where he was advised by Aaron Roth and Michael Kearns.
BibTeX
@inproceedings {257973,
author = {Ryan Rogers},
title = {A Differentially Private Data Analytics {API} at Scale},
booktitle = {2020 {USENIX} Conference on Privacy Engineering Practice and Respect ({PEPR} 20)},
year = {2020},
url = {https://www.usenix.org/conference/pepr20/presentation/rogers},
publisher = {{USENIX} Association},
month = oct,
}

Presentation Video