Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models
Amrita Roy Chowdhury, Theodoros Rekatsinas, Somesh Jha
Abstract
Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains such as medical diagnostics. In this paper, we present an algorithm for differentially-private learning of the parameters of a DGM. Our solution optimizes for the utility of inference queries over the DGM and \emph{adds noise that is customized to the properties of the private input dataset and the graph structure of the DGM}. To the best of our knowledge, this is the first explicit data-dependent privacy budget allocation algorithm in the context of DGMs. We compare our algorithm with a standard data-independent approach over a diverse suite of benchmarks and demonstrate that our solution requires a privacy budget that is roughly $3\times$ smaller to obtain the same or higher utility.
BibTeX
@InProceedings{pmlr-v119-chowdhury20a,
title = {Data-Dependent Differentially Private Parameter Learning for Directed Graphical Models},
author = {Chowdhury, Amrita Roy and Rekatsinas, Theodoros and Jha, Somesh},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {1939--1951},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/chowdhury20a/chowdhury20a.pdf},
url = {https://proceedings.mlr.press/v119/chowdhury20a.html},
abstract = {Directed graphical models (DGMs) are a class of probabilistic models that are widely used for predictive analysis in sensitive domains such as medical diagnostics. In this paper, we present an algorithm for differentially-private learning of the parameters of a DGM. Our solution optimizes for the utility of inference queries over the DGM and \emph{adds noise that is customized to the properties of the private input dataset and the graph structure of the DGM}. To the best of our knowledge, this is the first explicit data-dependent privacy budget allocation algorithm in the context of DGMs. We compare our algorithm with a standard data-independent approach over a diverse suite of benchmarks and demonstrate that our solution requires a privacy budget that is roughly $3\times$ smaller to obtain the same or higher utility.}
}