Christopher Bennett, AbdelRahman Abdou, and Paul C. van Oorschot (School of Computer Science, Carleton University, Canada)

Engines that scan Internet-connected devices allow for fast retrieval of useful information regarding said devices, and their running services. Examples of such engines include Censys and Shodan. We present a snapshot of our in-progress effort towards the characterization and systematic evaluation of such engines, herein focusing on results obtained from an empirical study that sheds light on several aspects. These include: the freshness of a result obtained from querying Censys and Shodan, the resources they consume from the scanned devices, and several interesting operational differences between engines observed from the network edge. Preliminary results confirm that the information retrieved from both engines can reflect updates within 24 hours, which aligns with implicit usage expectations in recent literature. The results also suggest that the consumed resources appear insignificant for common Internet applications, e.g., one full application-layer connection (banner grab) per port, per day. Results so far highlight the value of such engines to the research community

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POSEIDON: Privacy-Preserving Federated Neural Network Learning

Sinem Sav (EPFL), Apostolos Pyrgelis (EPFL), Juan Ramón Troncoso-Pastoriza (EPFL), David Froelicher (EPFL), Jean-Philippe Bossuat (EPFL), Joao Sa Sousa (EPFL), Jean-Pierre Hubaux (EPFL)

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“Lose Your Phone, Lose Your Identity”: Exploring Users’ Perceptions...

Michael Lutaaya, Hala Assal, Khadija Baig, Sana Maqsood, Sonia Chiasson (Carleton University)

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Who's Hosting the Block Party? Studying Third-Party Blockage of...

Marius Steffens (CISPA Helmholtz Center for Information Security), Marius Musch (TU Braunschweig), Martin Johns (TU Braunschweig), Ben Stock (CISPA Helmholtz Center for Information Security)

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