Classification of Personal Data Used by Personalised Robot Companions Based on Concern of Exposure

Lewis Riches, Kheng Lee Koay, Patrick Holthaus

Abstract: We present a paper looking at the accidental exposure of personal data by personalised companion robots in human-robot interaction. Due to the need for personal data, personalisation brings inherent risk of accidental personal data exposure through multi-modal communication. An online questionnaire was conducted to collect perceptions on the level of concern of personal data being exposed. The personal data examined in this paper has been used to personalise a companion robot along with links to the UK general data protection act. The level of concern for these personal data has been classified into high, medium, and low concern with guidelines provided on how these different classifications should be handled by a robot. Evidence has also been found that age, gender, extroversion, and conscientiousness influence a person’s perceptions on personal data exposure concern.