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Private and Continual Release of Statistics

  • T-H. Hubert Chan
  • Elaine Shi
  • Dawn Song
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6199)

Abstract

We ask the question – how can websites and data aggregators continually release updated statistics, and meanwhile preserve each individual user’s privacy? Given a stream of 0’s and 1’s, we propose a differentially private continual counter that outputs at every time step the approximate number of 1’s seen thus far. Our counter construction has error that is only poly-log in the number of time steps. We can extend the basic counter construction to allow websites to continually give top-k and hot items suggestions while preserving users’ privacy.

Keywords

Full Version Laplace Distribution Differential Privacy Traditional Setting True Count 
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 2010

Authors and Affiliations

  • T-H. Hubert Chan
    • 1
  • Elaine Shi
    • 2
  • Dawn Song
    • 3
  1. 1.The University of Hong Kong 
  2. 2.PARC 
  3. 3.UC Berkeley 

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