Pure Differential Privacy for Rectangle Queries via Private Partitions

  • Cynthia Dwork
  • Moni Naor
  • Omer Reingold
  • Guy N. RothblumEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9453)


We consider the task of data analysis with pure differential privacy. We construct new and improved mechanisms for statistical release of interval and rectangle queries. We also obtain a new algorithm for counting over a data stream under continual observation, whose error has optimal dependence on the data stream’s length.

A central ingredient in all of these result is a differentially private partition mechanism. Given set of data items drawn from a large universe, this mechanism outputs a partition of the universe into a small number of segments, each of which contain only a few of the data items.


Data Item Internal Node Partition Algorithm Differential Privacy Current Segment 
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

© International Association for Cryptologc Research 2015

Authors and Affiliations

  • Cynthia Dwork
    • 1
  • Moni Naor
    • 2
  • Omer Reingold
    • 3
  • Guy N. Rothblum
    • 3
    Email author
  1. 1.Microsoft ResearchMountain ViewUSA
  2. 2.The Weizmann InstituteRehovotIsrael
  3. 3.Samsung Research AmericaMountain ViewUSA

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