Practical locally private heavy hitters

Raef Bassily, Kobbi Nissim, Uri Stemmer, Abhradeep Thakurta

Research output: Contribution to journalConference articlepeer-review

Abstract

We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error - TreeHist and Bitstogram. In both algorithms, server running time is Õ(n) and user running time is Õ(1), hence improving on the prior state-of-the-art result of Bassily and Smith [STOC 2015] requiring Õ(n5/2) server time and Õ(n3/2) user time. With a typically large number of participants in local algorithms (n in the millions), this reduction in time complexity, in particular at the user side, is crucial for the use of such algorithms in practice. We implemented Algorithm TreeHist to verify our theoretical analysis and compared its performance with the performance of Google's RAPPOR code.

Original languageEnglish
Pages (from-to)2289-2297
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2017-December
StatePublished - 2017
Externally publishedYes
Event31st Annual Conference on Neural Information Processing Systems, NIPS 2017 - Long Beach, United States
Duration: 4 Dec 20179 Dec 2017

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