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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2204.09495 |
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| _version_ | 1866908720439492608 |
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| author | Rodriguez, David Del Alamo, Jose M. Cozar, Miguel Garcia, Boni |
| author_facet | Rodriguez, David Del Alamo, Jose M. Cozar, Miguel Garcia, Boni |
| contents | 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 ROI (Receiver Organization Identifier), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2204_09495 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | ROI: A method for identifying organizations receiving personal data Rodriguez, David Del Alamo, Jose M. Cozar, Miguel Garcia, Boni Cryptography and Security Machine Learning I.7.0; D.4.6 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 ROI (Receiver Organization Identifier), 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. |
| title | ROI: A method for identifying organizations receiving personal data |
| topic | Cryptography and Security Machine Learning I.7.0; D.4.6 |
| url | https://arxiv.org/abs/2204.09495 |