Differential Privacy of Hierarchical Census Data: An Optimization
Approach
- URL: http://arxiv.org/abs/2006.15673v2
- Date: Sun, 9 May 2021 20:02:56 GMT
- Title: Differential Privacy of Hierarchical Census Data: An Optimization
Approach
- Authors: Ferdinando Fioretto, Pascal Van Hentenryck, Keyu Zhu
- Abstract summary: Census Bureaus are interested in releasing aggregate socio-economic data about a large population without revealing sensitive information about any individual.
Recent events have identified some of the privacy challenges faced by these organizations.
This paper presents a novel differential-privacy mechanism for releasing hierarchical counts of individuals.
- Score: 53.29035917495491
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This paper is motivated by applications of a Census Bureau interested in
releasing aggregate socio-economic data about a large population without
revealing sensitive information about any individual. The released information
can be the number of individuals living alone, the number of cars they own, or
their salary brackets. Recent events have identified some of the privacy
challenges faced by these organizations. To address them, this paper presents a
novel differential-privacy mechanism for releasing hierarchical counts of
individuals. The counts are reported at multiple granularities (e.g., the
national, state, and county levels) and must be consistent across all levels.
The core of the mechanism is an optimization model that redistributes the noise
introduced to achieve differential privacy in order to meet the consistency
constraints between the hierarchical levels. The key technical contribution of
the paper shows that this optimization problem can be solved in polynomial time
by exploiting the structure of its cost functions. Experimental results on very
large, real datasets show that the proposed mechanism provides improvements of
up to two orders of magnitude in terms of computational efficiency and accuracy
with respect to other state-of-the-art techniques.
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