资源论文Practical Locally Private Heavy Hitters

Practical Locally Private Heavy Hitters

2020-02-10 | |  94 |   41 |   0

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 image.png and user running time is image.png, hence improving on the prior state-of-the-art result of Bassily and Smith [STOC 2015] requiring image.png server time and image.png 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.

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