TR-Model. A Metadata Profile Application for Personal Data Transparency

Abstract

People's usage of social networks, mobile applications, websites, sensor networks and other computer systems leads to a massive production of personal data about their behaviors and preferences. Personal data are used by organizations in business and marketing tasks. However, details about personal data usage are often not accessible or clear to data subject, raising concerns about privacy and security. Presentation of information about personal data usage needs improvement towards Personal Data Transparency. Thus, this paper aims to present the TR-Model, a Metadata Application Profile guideline that intends to propose a standardization on information to be considered minimally necessary to Personal Data Transparency as well as a set of specifications to guide developers on how to present this data. TR-Model elements are focused providing Personal Data Transparency in a user-friendly and high quality format. TR-Model presents a set of specification based on entities, metadata, metaevents and descriptions. The model evaluation was based on user testing in several scenarios of usage of personal data in a gym application tool. The information presented was created based on the TR-Model metadata, metaevents and descriptions. Participants evaluated transparency considering dimensions of Human-Computer Interaction and Information Quality. Participants' opinions were recorded in surveys and analyzed with descriptive statistics; the results indicate that the TR-Model was effective in supporting the production of friendly, understandable and relevant Transparency for data subjects, in compliance with regulations like GDPR.

Keywords

• Human-data interaction
• Metadata Application Profile
• Personal Data Transparency
• personal infovis
• user-friendly transparency