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Practical Locally Private Heavy Hitters (1707.04982v1)

Published 17 Jul 2017 in cs.DS

Abstract: We present new practical local differentially private heavy hitters algorithms achieving optimal or near-optimal worst-case error and running time -- 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 $O(n{5/2})$ 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, in particular at the user side, is crucial for making locally private heavy hitters algorithms usable in practice. We implemented Algorithm TreeHist to verify our theoretical analysis and compared its performance with the performance of Google's RAPPOR code.

Citations (214)

Summary

  • The paper presents a novel algorithm that efficiently identifies heavy hitters while ensuring local privacy.
  • It employs rigorous experimental evaluations to validate its methodology and demonstrate enhanced performance.
  • The research advances practical applications in privacy-preserving data analytics and sets the stage for future innovations.

Essay on the Provided Academic Paper

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Introduction

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Methodology

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Results

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Conclusion

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