Open AccessLinkedIn's Audience Engagements API
Author(s) -
Ryan Rogers,
Subbu Subramaniam,
Sean Peng,
David Durfee,
Seung–Hyun Lee,
Santosh Kumar Kancha,
Shraddha Sahay,
Parvez Ahammad
Publication year - 2021
Publication title -
the journal of privacy and confidentiality
Language(s) - English
Resource type - Journals
ISSN - 2575-8527
DOI - 10.29012/jpc.782
Subject(s) - differential privacy , analytics , computer science , internet privacy , service (business) , privacy software , information privacy , product (mathematics) , computer security , business , data science , marketing , data mining , geometry , mathematics
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.