Practical Locally Private Heavy Hitters
نویسندگان
چکیده
We present new heavy-hitters algorithms satisfying local-differential-privacy, with optimal or nearoptimal worst-case error, running time, and memory. In our algorithms, the 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 $O(n^{5/2})$ server time and $O(n^{3/2})$ user time. With a typically large number of participants in local algorithms ($n$ in the millions), this reduction in time complexity is crucial for making locally-private heavy-hitters algorithms usable in practice.
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