From data flows to privacy‐benefit trade‐offs: A user‐centric semantic model

Abstract

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.

Visualising Personal Data Flows: Insights from a Case Study of Booking.com

“…To better facilitate the personal data flow mapping and visualisation, we adopted a simplified version of the graphical model proposed in [10] with the main aim of establishing the relationships between the following entities of different types: a) 'Person' entities stand for natural people in the physical world; b) 'Data' entities refer to atomic (personal) data items about one or more person entities; c) 'Service' entities refer to different physical and online services that serve people for a specific purpose; d) 'Organisation' entities refer to organisations that relate to one or more services. We analysed the privacy policy from the perspective of how data entities flow from users of Booking.com to different dataconsuming entities including Booking.com and other organisation entities.…”

“…In addition, another way to consolidate the understanding of privacy and personal data collection/sharing is to develop graphical models. More recently, a graphical model proposed by Lu and Li [10] can evaluate personal data flows from "me" (a specific user) and values flowing back to "me" to help inform "me" about privacy-benefit trade-offs.…”

Introduction to the Minitrack on Culture, Identity, and Inclusion

“…Finally, besides norms, information distribution in a context may have privacy-benefit trade-offs (Lu & Li, 2022). Some users may choose to break an information flow or collection norm to receive desired benefits.…”