Demo Data from EDBT 2026 / reviewers in the wild / expert
Friedemann Lipphardt
dblp:414/3975
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2026
0009-0006-9412-6844ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 first-author · 2 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
Trustworthy machine learning · 50% Language models and text generation · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Machine learning › Trustworthy machine learning
content moderation |
1.0 | 1 | 2026 | There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models · NDSS 2026 |
Natural language and speech › Language models and text generation
large language model safety |
1.0 | 1 | 2026 | There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models · NDSS 2026 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | There is No War in Ba Sing Se: A Global Analysis of Content Moderation in Large Language Models
Friedemann Lipphardt, Moonis Ali, Martin Banzer, Anja Feldmann, Devashish Gosain |
NDSS | 1 |
| 2025 | Can You Hear Me? A First Study of VoIP Censorship Techniques in Saudi Arabia and the UAEabstractInternet censorship is a well-explored area, typically focusing on Web censorship enacted by powerful nation-states like China and Russia. In this paper, we diverge from the norm and study the unexplored VoIP censorship prevalent in the Middle East for over a decade. We present the first research analyzing the VoIP filtering mechanics deployed nationwide. Based on extensive on-the-ground experiments, our investigation reveals novel censorship techniques for distinctly filtering VoIP traffic originating from specific apps.We meticulously analyze the VoIP traffic of nine popular apps and reveal the presence of sophisticated "middleboxes" placed within the local ISPs. These middleboxes possess the unique capability to selectively block the calling facility, leaving the rest of the apps’ features, such as texting and media sharing, completely unhindered. Unlike traditional Web filtering techniques that often require inspecting DNS and HTTP(s) traffic, our research demonstrates that the middlebox scrutinizes the STUN protocol requests and response packets to individually identify the application-specific VoIP call flow. Our analysis of widely used apps such as WhatsApp, Signal, and Facebook Messenger reveals a uniquely characterizable flow of STUN packets. We experimentally confirm that the censor exploits such fingerprints to block VoIP calls made through these apps. Furthermore, our experiments unveil additional nuances in censorship tactics. For apps like LINE, the middlebox searches for the VoIP server IP address in some selective packets. On a successful match, it drops the packets, effectively disrupting the call.In essence, our findings provide valuable insights into the intricate mechanisms of sophisticated VoIP filtering techniques, paving the way for more informed approaches to combating such censorship practices. Friedemann Lipphardt, Anja Feldmann, Devashish Gosain |
EuroS&P | 1 |