LinkedIn Salary: A System for Secure Collection and Presentation of Structured Compensation Insights to Job Seekers
This system addresses the problem of salary transparency for job seekers on LinkedIn, though it is incremental as it applies existing methods to a new domain-specific dataset.
The paper tackles the challenge of providing reliable salary information to job seekers by developing LinkedIn Salary, a system that securely collects and processes compensation data from over 1.5 million members, balancing privacy and modeling needs.
Online professional social networks such as LinkedIn have enhanced the ability of job seekers to discover and assess career opportunities, and the ability of job providers to discover and assess potential candidates. For most job seekers, salary (or broadly compensation) is a crucial consideration in choosing a new job. At the same time, job seekers face challenges in learning the compensation associated with different jobs, given the sensitive nature of compensation data and the dearth of reliable sources containing compensation data. Towards the goal of helping the world's professionals optimize their earning potential through salary transparency, we present LinkedIn Salary, a system for collecting compensation information from LinkedIn members and providing compensation insights to job seekers. We present the overall design and architecture, and describe the key components needed for the secure collection, de-identification, and processing of compensation data, focusing on the unique challenges associated with privacy and security. We perform an experimental study with more than one year of compensation submission history data collected from over 1.5 million LinkedIn members, thereby demonstrating the tradeoffs between privacy and modeling needs. We also highlight the lessons learned from the production deployment of this system at LinkedIn.