A model for system developers to measure the privacy risk of data

TL;DR

This paper introduces a model for system developers to quantify perceived user privacy risks based on data sensitivity, visibility, and relevance, aiding in privacy-aware system design.

Contribution

The paper presents a new model for measuring perceived privacy risk and validates it through a user survey, providing practical insights for system developers.

Findings

Perceived privacy risk increases with data sensitivity and visibility.

Perceived privacy risk decreases with data relevance to the application.

Default visibility of data significantly impacts perceived privacy risk.

Abstract

In this paper, we propose a model that could be used by system developers to measure the privacy risk perceived by users when they disclose data into software systems. We first derive a model to measure the perceived privacy risk based on existing knowledge and then we test our model through a survey with 151 participants. Our findings revealed that users' perceived privacy risk monotonically increases with data sensitivity and visibility, and monotonically decreases with data relevance to the application. Furthermore, how visible data is in an application by default when the user discloses data had the highest impact on the perceived privacy risk. This model would enable developers to measure the users' perceived privacy risk associated with data items, which would help them to understand how to treat different data within a system design.