Yun Shen (NortonLifeLock Research Group), Pierre-Antoine Vervier (NortonLifeLock Research Group), Gianluca Stringhini (Boston University)

Mobile phones enable the collection of a wealth of private information, from unique identifiers (e.g., email addresses), to a user’s location, to their text messages. This information can be harvested by apps and sent to third parties, which can use it for a variety of purposes. In this paper we perform the largest study of private information collection (PIC) on Android to date. Leveraging an anonymized dataset collected from the customers of a popular mobile security product, we analyze the flows of sensitive information generated by 2.1M unique apps installed by 17.3M users over a period of 21 months between 2018 and 2019. We find that 87.2% of all devices send private information to at least five different domains, and that actors active in different regions (e.g., Asia compared to Europe) are interested in collecting different types of information. The United States (62% of the total) and China (7% of total flows) are the countries that collect most private information. Our findings raise issues regarding data regulation, and would encourage policymakers to further regulate how private information is used by and shared among the companies and how accountability can be truly guaranteed.

View More Papers

Safer Illinois and RokWall: Privacy Preserving University Health Apps...

Vikram Sharma Mailthody, James Wei, Nicholas Chen, Mohammad Behnia, Ruihao Yao, Qihao Wang, Vedant Agarwal, Churan He, Lijian Wang, Leihao Chen, Amit Agarwal, Edward Richter, Wen-mei Hwu, and Christopher Fletcher (University of Illinois at Urbana-Champaign); Jinjun Xiong (IBM); Andrew Miller and Sanjay Patel (University of Illinois at Urbana-Champaign)

Read More

SerialDetector: Principled and Practical Exploration of Object Injection Vulnerabilities...

Mikhail Shcherbakov (KTH Royal Institute of Technology), Musard Balliu (KTH Royal Institute of Technology)

Read More

Manipulating the Byzantine: Optimizing Model Poisoning Attacks and Defenses...

Virat Shejwalkar (UMass Amherst), Amir Houmansadr (UMass Amherst)

Read More

Measuring DoT/DoH Blocking Using OONI Probe: a Preliminary Study

S. Basso (Open Observatory of Network Interference)

Read More