Secure Multi-party Computation of Differentially Private Heavy Hitters
Jonas Böhler, Florian Kerschbaum · 2021
Private learning of top-k, i.e., the k most frequent values also called heavy hitters, is a common industry scenario: Companies want to privately learn, e.g., frequently typed new words to improve suggestions on mobile devices, often used browser settings, telemetry data of frequent crashes, heavily shared articles, etc.