Apache® Spark™ News

Detecting Abuse at Scale: Locality Sensitive Hashing at Uber Engineering

With 5 million Uber trips taken daily by users worldwide, it is important for Uber engineers to ensure that data is accurate. If used correctly, metadata and aggregate data can quickly detect platform abuse, from spam to fake accounts and payment fraud. Amplifying the right data signals makes detection more precise and thus, more reliable. In this article, we will demonstrate how this powerful tool is used by Uber to detect fraudulent trips at scale.