  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.
