Distributed Private Heavy Hitters

  • Justin Hsu
  • Sanjeev Khanna
  • Aaron Roth
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7391)


In this paper, we give efficient algorithms and lower bounds for solving the heavy hitters problem while preserving differential privacy in the fully distributed local model. In this model, there are n parties, each of which possesses a single element from a universe of size N. The heavy hitters problem is to find the identity of the most common element shared amongst the n parties. In the local model, there is no trusted database administrator, and so the algorithm must interact with each of the n parties separately, using a differentially private protocol. We give tight information-theoretic upper and lower bounds on the accuracy to which this problem can be solved in the local model (giving a separation between the local model and the more common centralized model of privacy), as well as computationally efficient algorithms even in the case where the data universe N may be exponentially large.


Hash Function Local Model Full Version Sparse Recovery Heavy Hitter 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Justin Hsu
    • 1
  • Sanjeev Khanna
    • 1
  • Aaron Roth
    • 1
  1. 1.University of PennsylvaniaPhiladelphiaUSA

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