Lifting in Support of Privacy-Preserving Probabilistic Inference

Gehrke, MarcelLiebenow, JohannesMohammadi, EsfandiarBraun, Tanya2025-01-132025-01-1320241610-1987http://dx.doi.org/10.1007/s13218-024-00851-yhttps://dl.gi.de/handle/20.500.12116/45574Privacy-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.Lifting in Support of Privacy-Preserving Probabilistic InferenceText/Journal Article10.1007/s13218-024-00851-y