ROI: a method for identifying organizations receiving personal data

• Published in: Computing Vol. 106; no. 1; pp. 163 - 184
• Main Authors: Rodriguez, David, Del Alamo, Jose M., Cozar, Miguel, García, Boni
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 2024; Springer Nature B.V
• Subjects: Applications programs; Artificial Intelligence; Computer Appl. in Administrative Data Processing; Computer Communication Networks; Computer Science; Data transparency; Domains; Information Systems Applications (incl.Internet); Mobile computing; Mobile operating systems; Organizations; Personal information; Privacy; Regular Paper; Software Engineering; Third party; 68P27; Company; 68U15; Data controller; Third-party; Identification; Apps; Personal data; Android; Privacy; 68M11; NER; Domain; 68M15; 68M25; 68M14; 68T50; 68M12
• ISSN: 0010-485X; 1436-5057
• DOI: 10.1007/s00607-023-01209-2

Many studies have exposed the massive collection of personal data in the digital ecosystem through, for instance, websites, mobile apps, or smart devices. This fact goes unnoticed by most users, who are also unaware that the collectors are sharing their personal data with many different organizations around the globe. This paper assesses techniques available in the state of the art to identify the organizations receiving this personal data. Based on our findings, we propose Receiver Organization Identifier (ROI), a fully automated method that combines different techniques to achieve a 95.71% precision score in identifying an organization receiving personal data. We demonstrate our method in the wild by evaluating 10,000 Android apps and exposing the organizations that receive users’ personal data. We further assess the transparency of these data-sharing practices by analyzing the apps’ privacy policies. The results reveal a concerning lack of transparency in almost 78% of apps, suggesting the need for regulators to take action.