  Memorization of the relation between entities in a dataset can lead to
privacy issues when using a trained model for question answering. We introduce
Relational Memorization (RM) to understand, quantify and control this
phenomenon. While bounding general memorization can have detrimental effects on
the performance of a trained model, bounding RM does not prevent effective
learning. The difference is most pronounced when the data distribution is
long-tailed, with many queries having only few training examples: Impeding
general memorization prevents effective learning, while impeding only
relational memorization still allows learning general properties of the
underlying concepts. We formalize the notion of Relational Privacy (RP) and,
inspired by Differential Privacy (DP), we provide a possible definition of
Differential Relational Privacy (DrP). These notions can be used to describe
and compute bounds on the amount of RM in a trained model. We illustrate
Relational Privacy concepts in experiments with large-scale models for Question
Answering.
