Lifting in Support of Privacy-Preserving Probabilistic Inference
Marcel Gehrke, Johannes Liebenow, Esfandiar Mohammadi, Tanya Braun · Künstliche Intell. · 2024
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.