Differentially private hierarchical countofcounts histograms

Yu Hsuan Kuo, Cho Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala

Research output: Contribution to journalConference articlepeer-review

4 Scopus citations

Abstract

We consider the problem of privately releasing a class of queries that we call hierarchical count-of-counts histograms. Count-of-counts histograms partition the rows of an input table into groups (e.g., group of people in the same house- hold), and for every integer j report the number of groups of size j. Hierarchical count-of-counts queries report count-of- counts histograms at different granularities as per hierarchy defined on an attribute in the input data (e.g., geographical location of a household at the national, state and county levels). In this paper, we introduce this problem, along with appropriate error metrics and propose a differentially private solution that generates count-of-counts histograms that are consistent across all levels of the hierarchy.

Original languageEnglish (US)
Pages (from-to)1509-1521
Number of pages13
JournalProceedings of the VLDB Endowment
Volume11
Issue number11
DOIs
StatePublished - 2018
Event44th International Conference on Very Large Data Bases, VLDB 2018 - Rio de Janeiro, Brazil
Duration: Aug 27 2017Aug 31 2017

All Science Journal Classification (ASJC) codes

  • Computer Science (miscellaneous)
  • Computer Science(all)

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