From Data Flows to Privacy-Benefit Trade-offs: A User-Centric Semantic Model

In today's highly connected cyber-physical world, people are constantly disclosing personal and sensitive data to different organizations and other people through the use of online and physical services. This is because sharing personal information can bring various benefits for themselves and others. However, data disclosure activities can lead to unexpected privacy issues, and there is a general lack of tools that help to improve users' awareness of the subtle privacy-benefit trade-offs and to make more informed decisions on their data disclosure activities in wider contexts. To fill this gap, this paper presents a novel user-centric, data-flow graph based semantic model, which can show how a given user's personal and sensitive data have been disclosed to different entities and what benefits the user gained through such data disclosures. The model allows automatic analysis of privacy-benefit trade-offs around a target user's data sharing activities, therefore it can support development of user-centric software tools for people to better manage their data disclosure activities to achieve a better balance between privacy and benefits in the cyber-physical world.