PriPeARL: A Framework for Privacy-Preserving Analytics and Reporting at LinkedIn

By Krishnaram Kenthapadi, Thanh T. L. Tran. 2018

39 citations recorded by the source corpus.

Preserving privacy of users is a key requirement of web-scale analytics and reporting applications, and has witnessed a renewed focus in light of recent data breaches and new regulations such as GDPR. We focus on the problem of computing robust, reliable analytics in a privacy-preserving manner, while satisfying product requirements. We present PriPeARL, a framework for privacy-preserving analytics and reporting, inspired by differential privacy. We describe the overall design and architecture, and the key modeling components, focusing on the unique challenges associated with privacy, coverage, utility, and consistency. We perform an experimental study in the context of ads analytics and reporting at LinkedIn, thereby demonstrating the tradeoffs between privacy and utility needs, and the applicability of privacy-preserving mechanisms to real-world data. We also highlight the lessons learned from the production deployment of our system at LinkedIn.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peter Kairouz
2. H. Brendan McMahan
3. Ryan M. Rogers
4. Santosh Kumar Kancha
5. Parvez Ahammad
6. Wennan Zhu
7. Sean Peng
8. Seunghyun Lee

In Computer Science Applications

1. H. Brendan McMahan
2. Peter Kairouz
3. Ryan M. Rogers

In Sociology and Political Science

1. H. Brendan McMahan
2. Wei Li
3. Haicheng Sun
4. Peter Kairouz
5. Ryan M. Rogers

Citations in the corpus

• Federated Heavy Hitters Discovery with Differential Privacy (2019, methodology): “Differential privacy (DP) is a rigorous privacy notion that has been carefully studied over the last decade (Dwork et al., 2006b,a; Dwork, 2008; Dwork and Roth, 2014) and widely adopted in industry (Ding et al., 2017; Apple, 2017; Kenthapadi and Tran, 2018; Erlingsson et al., 2014).”
• LinkedIn's Audience Engagements API: A Privacy Preserving Data Analytics System at Scale (2020, methodology): “In this less restrictive privacy setting, the main industrial differential privacy systems include Microsoft’s PINQ [6], Uber’s FLEX for its internal analytics [7], LinkedIn’s PriPeARL for its ad analytics [8], Google’s recent differential privacy open source project [9, 10], and the 2020 U.”