Data-driven value extraction and human well-being under EU law

2022 | Electronic Markets | Citations: 0

Authors: Trzaskowski, Jan

Abstract: In this article, we explore the intersection between data protection law and con ... Expand

Abstract: In this article, we explore the intersection between data protection law and consumer protection law in their application to data-driven business models that monetise attention and personal data. We approach European Union law from a constitutional perspective, as in addition to economic harms some aspects of predatory business models also pose a risk for human dignity, human well-being, social welfare and democracy. From a three-tiered model of information asymmetry, it is argued that there is a need to rethink consumer protection in order to restore some sense of equity in commercial interactions online. In that vein Social welfare computing can play an important role in combating superficiality and strengthening social cohesion. Collapse

Topics: data-driven business model privacy social media IS economics societal evaluation criteria

Methods: machine learning archival research literature study computational algorithm

Theories: lemon market theory

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

2019 | HICSS | Citations: 0

Authors: Senarath, Awanthika; Grobler, Marthie; Arachchilage, Nalin A G

Abstract: In this paper, we propose a model that could be used by system developers to mea ... Expand

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. Collapse

Topics: privacy software developer personality electronic mail systems design

Methods: survey focus group survey design personal interview grounded theory