A Model for System Developers to Measure the Privacy Risk of Data

Senarath, Awanthika
Grobler, Marthie
Arachchilage, Nalin
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In this paper, we propose a model that could be used by system developers to measure the perceived privacy risk of 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.
Information Security and Privacy in Business and Society, Organizational Systems and Technology, privacy, privacy risk, software development, understanding data
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