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Practical Locally Private Heavy Hitters
Raef Bassily · Kobbi Nissim · Uri Stemmer · Abhradeep Guha Thakurta

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #65
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 $\tilde O(n)$ and user running time is $\tilde O(1)$, hence improving on the prior state-of-the-art result of Bassily and Smith [STOC 2015] requiring $\tilde O(n^{5/2})$ server time and $\tilde O(n^{3/2})$ 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.

Author Information

Raef Bassily (The Ohio State University)
Kobbi Nissim (Georgetown University)
Uri Stemmer (Harvard University)
Abhradeep Guha Thakurta (University of California Santa Cruz)

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