Abstrakti

While generation of synthetic data under differential privacy (DP) has received a lot of attention in the data privacy community, analysis of synthetic data has received much less. Existing work has shown that simply analysing DP synthetic data as if it were real does not produce valid inferences of population-level quantities. For example, confidence intervals become too narrow, which we demonstrate with a simple experiment. We tackle this problem by combining synthetic data analysis techniques from the field of multiple imputation (MI), and synthetic data generation using noise-aware (NA) Bayesian modeling into a pipeline NA+MI that allows computing accurate uncertainty estimates for population-level quantities from DP synthetic data. To implement NA+MI for discrete data generation using the values of marginal queries, we develop a novel noise-aware synthetic data generation algorithm NAPSU-MQ using the principle of maximum entropy. Our experiments demonstrate that the pipeline is able to produce accurate confidence intervals from DP synthetic data. The intervals become wider with tighter privacy to accurately capture the additional uncertainty stemming from DP noise.
AlkuperäiskieliEnglanti
OtsikkoProceedings of The 26th International Conference on Artificial Intelligence and Statistics (AISTATS) 2023
ToimittajatFrancisco Ruiz, Jennifer Dy, Jan-Willem van de Meent
KustantajaJMLR
Sivut3620-3643
TilaJulkaistu - 2023
OKM-julkaisutyyppiA3 Kirjan tai muun kokoomateoksen osa
TapahtumaInternational Conference on Artificial Intelligence and Statistics - Valencia, Espanja
Kesto: 25 huhtik. 202327 huhtik. 2023
Konferenssinumero: 26
http://aistats.org/aistats2023/

Julkaisusarja

NimiProceedings of Machine Learning Research
KustantajaJMLR
Vuosikerta206
ISSN (painettu)2640-3498

Conference

ConferenceInternational Conference on Artificial Intelligence and Statistics
LyhennettäAISTATS
Maa/AlueEspanja
KaupunkiValencia
Ajanjakso25/04/202327/04/2023
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