Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy

Optimizing Error of High-Dimensional Statistical Queries Under Differential Privacy

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In this work we describe the High-Dimensional Matrix Mechanism (HDMM), a differentially private algorithm for answering a workload of predicate counting queries. HDMM represents query workloads using a compact implicit matrix representation and exploits this representation to efficiently optimize...

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Bibliographic Details
Main Authors:	Ryan McKenna, Gerome Miklau, Michael Hay, Ashwin Machanavajjhala
Format:	Article
Language:	English
Published:	Labor Dynamics Institute 2023-08-01
Series:	The Journal of Privacy and Confidentiality
Subjects:	differential privacy linear queries workload high-dimensional matrix-mechanism marginals
Online Access:	https://journalprivacyconfidentiality.org/index.php/jpc/article/view/791

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