Lifting in Support of Privacy-Preserving Probabilistic Inference

dc.contributor.author	Gehrke, Marcel
dc.contributor.author	Liebenow, Johannes
dc.contributor.author	Mohammadi, Esfandiar
dc.contributor.author	Braun, Tanya
dc.date	2024-11-01
dc.date.accessioned	2025-01-13T11:15:16Z
dc.date.available	2025-01-13T11:15:16Z
dc.date.issued	2024
dc.description.abstract	Privacy-preserving inference aims to avoid revealing identifying information about individuals during inference. Lifted probabilistic inference works with groups of indistinguishable individuals, which has the potential to prevent tracing back a query result to a particular individual in a group. Therefore, we investigate how lifting, by providing anonymity, can help preserve privacy in probabilistic inference. Specifically, we show correspondences between k -anonymity and lifting and present s-symmetry as an analogue as well as PAULI, a privacy-preserving inference algorithm that ensures s-symmetry during query answering.	de
dc.identifier.doi	10.1007/s13218-024-00851-y
dc.identifier.issn	1610-1987
dc.identifier.uri	http://dx.doi.org/10.1007/s13218-024-00851-y
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/45574
dc.publisher	Springer
dc.relation.ispartof	KI - Künstliche Intelligenz: Vol. 38, No. 3
dc.relation.ispartofseries	KI - Künstliche Intelligenz
dc.title	Lifting in Support of Privacy-Preserving Probabilistic Inference	de
dc.type	Text/Journal Article
mci.reference.pages	225-241

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