Differentially Private Hierarchical Count-of-Counts Histograms
Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin, Machanavajjhala

TL;DR
This paper introduces a differentially private method for releasing hierarchical count-of-counts histograms, ensuring consistency across multiple levels of data granularity while maintaining privacy.
Contribution
It formalizes the problem of differentially private hierarchical count-of-counts histograms and proposes a novel solution that guarantees consistency across hierarchy levels.
Findings
Provides a differentially private algorithm for hierarchical histograms
Ensures consistency of count-of-counts histograms across hierarchy levels
Achieves accurate private data release with formal error metrics
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 household), 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.
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Data Management and Algorithms · Data Quality and Management
