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Emiliano De Cristofaro
Researcher at University College London
Publications - 262
Citations - 9897
Emiliano De Cristofaro is an academic researcher from University College London. The author has contributed to research in topics: Social media & Computer science. The author has an hindex of 47, co-authored 251 publications receiving 7263 citations. Previous affiliations of Emiliano De Cristofaro include Boston University & Nokia.
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Characterizing the use of images by state-sponsored troll accounts on Twitter
TL;DR: This work analyzes a ground truth dataset of 1.8M images posted to Twitter by called Russian trolls to provide new insight into how state-sponsored trolls operate, and specifically how they use imagery to achieve their goals.
Proceedings ArticleDOI
Privacy-friendly mobility analytics using aggregate location data
TL;DR: This paper studies the feasibility of crowd-sourced mobility analytics over aggregate location information, using a privacy-preserving aggregation protocol, and presents and evaluates a mobile app prototype, called Mobility Data Donors, in terms of computation, communication, and energy overhead, demonstrating the real-world deployability of the techniques.
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Harvesting SSL Certificate Data to Identify Web-Fraud
TL;DR: In this paper, the authors conduct a comprehensive study of SSL certificates and build a classifier that detects web-fraud domains with high accuracy, based on extensive measurements, which is used to detect typosquatting and phishing.
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Privacy-Friendly Mobility Analytics using Aggregate Location Data
TL;DR: In this article, the authors study the feasibility of crowd-sourced mobility analytics over aggregate location information, where users periodically report their location, using a privacy-preserving aggregation protocol, so that the server can only recover aggregates, i.e., how many but not which, users are in a region at a given time.
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Linear-Complexity Private Set Intersection Protocols Secure in Malicious Model.
TL;DR: In this article, the authors proposed a private set intersection (PSI) protocol that is secure in the malicious model under standard cryptographic assumptions, with both linear communication and computational complexities.