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.

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Towards Measuring Supply Chain Attacks on Package Managers for...

Ruian Duan (Georgia Institute of Technology), Omar Alrawi (Georgia Institute of Technology), Ranjita Pai Kasturi (Georgia Institute of Technology), Ryan Elder (Georgia Institute of Technology), Brendan Saltaformaggio (Georgia Institute of Technology), Wenke Lee (Georgia Institute of Technology)

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RandRunner: Distributed Randomness from Trapdoor VDFs with Strong Uniqueness

Philipp Schindler (SBA Research), Aljosha Judmayer (SBA Research), Markus Hittmeir (SBA Research), Nicholas Stifter (SBA Research, TU Wien), Edgar Weippl (Universität Wien)

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Cross-National Study on Phishing Resilience

Shakthidhar Reddy Gopavaram (Indiana University), Jayati Dev (Indiana University), Marthie Grobler (CSIRO’s Data61), DongInn Kim (Indiana University), Sanchari Das (University of Denver), L. Jean Camp (Indiana University)

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FlowLens: Enabling Efficient Flow Classification for ML-based Network Security...

Diogo Barradas (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Nuno Santos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Luis Rodrigues (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), Salvatore Signorello (LASIGE, Faculdade de Ciências, Universidade de Lisboa), Fernando M. V. Ramos (INESC-ID, Instituto Superior Técnico, Universidade de Lisboa), André Madeira (INESC-ID, Instituto Superior Técnico, Universidade de…

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