Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data

Miguel Fuentes, Brett C. Mullins, Ryan McKenna, Gerome Miklau, Daniel Sheldon
Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, PMLR 238:2404-2412, 2024.

Abstract

Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism JAM-PGM, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that JAM-PGM is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.

Cite this Paper

BibTeX
@InProceedings{pmlr-v238-fuentes24a, title = {Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data}, author = {Fuentes, Miguel and Mullins, Brett C. and McKenna, Ryan and Miklau, Gerome and Sheldon, Daniel}, booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics}, pages = {2404--2412}, year = {2024}, editor = {Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen}, volume = {238}, series = {Proceedings of Machine Learning Research}, month = {02--04 May}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v238/fuentes24a/fuentes24a.pdf}, url = {https://proceedings.mlr.press/v238/fuentes24a.html}, abstract = {Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism JAM-PGM, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that JAM-PGM is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.} }
Endnote
%0 Conference Paper %T Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data %A Miguel Fuentes %A Brett C. Mullins %A Ryan McKenna %A Gerome Miklau %A Daniel Sheldon %B Proceedings of The 27th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2024 %E Sanjoy Dasgupta %E Stephan Mandt %E Yingzhen Li %F pmlr-v238-fuentes24a %I PMLR %P 2404--2412 %U https://proceedings.mlr.press/v238/fuentes24a.html %V 238 %X Mechanisms for generating differentially private synthetic data based on marginals and graphical models have been successful in a wide range of settings. However, one limitation of these methods is their inability to incorporate public data. Initializing a data generating model by pre-training on public data has shown to improve the quality of synthetic data, but this technique is not applicable when model structure is not determined a priori. We develop the mechanism JAM-PGM, which expands the adaptive measurements framework to jointly select between measuring public data and private data. This technique allows for public data to be included in a graphical-model-based mechanism. We show that JAM-PGM is able to outperform both publicly assisted and non publicly assisted synthetic data generation mechanisms even when the public data distribution is biased.
APA
Fuentes, M., Mullins, B.C., McKenna, R., Miklau, G. & Sheldon, D.. (2024). Joint Selection: Adaptively Incorporating Public Information for Private Synthetic Data. Proceedings of The 27th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 238:2404-2412 Available from https://proceedings.mlr.press/v238/fuentes24a.html.

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