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This content will become publicly available on December 15, 2026

Title: Efficient and Private Marginal Reconstruction with Local Non-Negativity
Differential privacy is the dominant standard for formal and quantifiable privacy and has been used in major deployments that impact millions of people. Many differentially private algorithms for query release and synthetic data contain steps that reconstruct answers to queries from answers to other queries that have been measured privately. Reconstruction is an important subproblem for such mecha- nisms to economize the privacy budget, minimize error on reconstructed answers, and allow for scalability to high-dimensional datasets. In this paper, we introduce a principled and efficient postprocessing method ReM (Residuals-to-Marginals) for reconstructing answers to marginal queries. Our method builds on recent work on efficient mechanisms for marginal query release, based on making measurements using a residual query basis that admits efficient pseudoinversion, which is an important primitive used in reconstruction. An extension GReM-LNN (Gaussian Residuals-to-Marginals with Local Non-negativity) reconstructs marginals under Gaussian noise satisfying consistency and non-negativity, which often reduces error on reconstructed answers. We demonstrate the utility of ReM and GReM-LNN by applying them to improve existing private query answering mechanisms.  more » « less
Award ID(s):
2317232 1931686
PAR ID:
10574594
Author(s) / Creator(s):
; ; ; ; ;
Publisher / Repository:
38th Conference on Neural Information Processing Systems (NeurIPS 2024).
Date Published:
ISSN:
0000-0000
Format(s):
Medium: X
Location:
38th Conference on Neural Information Processing Systems (NeurIPS 2024).
Sponsoring Org:
National Science Foundation
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