Our members place their trust in us, and expect and deserve a safe and trusted community where they can express themselves professionally. We are constantly investing in a variety of systems (Automated Fake Account Detection at LinkedIn, Defending Against Abuse at LinkedIn’s Scale, and How We’re Protecting Members From Fake Profiles) to help automatically detect...
trust engineering Articles
-
-
The Anti-Abuse AI Team at LinkedIn creates, deploys, and maintains models that detect and prevent various types of abuse, including the creation of fake accounts, member profile scraping, automated spam, and account takeovers. There are several unique challenges we face when using machine learning to prevent abuse on a large professional network, including:...
- Topics:
- machine learning,
- Open Source,
- trust engineering
-
At LinkedIn, we are entrusted with protecting the privacy and data of our members. Storing members’ choices on how they intend their data to be used is vital to building and maintaining member trust, as well as ensuring compliance with legal and regulatory requirements. It is also essential that we respect members’ preferences for how they wish to interact with...
- Topics:
- infrastructure,
- scale,
- trust engineering
-
As members explore the many products of LinkedIn, including the feed, homepage, Learning, Recruiter, and Sales Navigator, to name a...
-
Co-authors: Krishnaram Kenthapadi, Thanh Tran, Mark Dietz, and Ian Koeppe Preserving privacy of users is a key requirement of...
- Topics:
- trust engineering,
- research,
- Privacy,
- Analytics
-
LinkedIn members entrust us with their personal data and we are committed to working hard every day to maintain that trust within a...
- Topics:
- scale,
- Security,
- trust engineering