Differentially Private Hierarchical Count-of-Counts Histograms
By Yu-Hsuan Kuo, Cho-Chun Chiu, Daniel Kifer, Michael Hay, Ashwin Machanavajjhala. 2018
31 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Zhiwei Steven Wu
- Matthew Joseph
- Jieming Mao
- Janardhan Kulkarni
- Aaron Roth
- Jonathan Ullman
- Michael Kearns
- Ravi Kumar
Citations in the corpus
- Locally Private Gaussian Estimation (2018, background): “Since its introduction [11], a rich line of work has made differential privacy a compelling privacy guarantee (see Dwork et al. [12] and Vadhan [24] for surveys), and deployments of differential privacy now exist at many organizations, including Apple [2], Google [5, 13], Microsoft [8], Mozilla [3],”