Lifting in Support of Privacy-preserving Probabilistic Inference

Gehrke, Marcel; Liebenow, Johannes; Mohammadi, Esfandiar; Braun, Tanya

Abstract

The aim of privacy-preserving inference is 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.