A model for personalised perception of policies
Anirban Basu, Stephen Marsh, Mohammad Shahriar Rahman, and Shinsaku Kiyomoto
We are often presented with policy terms that we agree with but are unable to gauge our personal perceptions (e.g., in terms of associated risks) of those terms. In some cases, although partial agreement is acceptable (e.g., allowing a mobile application to access specific resources), one is unable to quantify, even in relative terms, perceptions such as the risks to one’s privacy. There has been research done in the area of privacy risk quantification, especially around data release, which present macroscopic views of the risks of re-identication of an individual. In this position paper, we propose a novel model for the personalized perception, using privacy risk perception as an example, of policy terms from an individual’s viewpoint. In order to cater for inconsistencies of opinion, our model utilises the building blocks of the analytic hierarchy process and concordance correlation. The quantification of perception is idiosyncratic, hence can be seen as a measure for trust empowerment. It can also help a user compare and evaluate different policies as well as the impacts of partial agreement of terms. While we discuss the perception of risk in this paper, our model is applicable to perception of any other qualitative and emotive feature or thought associated with a policy.