Demo Data from SIGIR 2026 / reviewers in the wild / expert
Doguhan Yeke
dblp:385/6396
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2025
0000-0003-1987-6830ORCID · 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.
| Network and information security
2 papers |
Authentication and access control · 48% Cyber-physical and IoT security · 28% Web and mobile security · 24% | |
| Artificial intelligence
1 paper |
Robot navigation and mapping · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
multi-robot navigation |
0.9 | 1 | 2025 | Automated Discovery of Semantic Attacks in Multi-Robot Navigation Systems · USENIX Security Symposium 2025 |
Cyber-physical and IoT security › deception attacks
semantic attack |
0.9 | 1 | 2025 | Automated Discovery of Semantic Attacks in Multi-Robot Navigation Systems · USENIX Security Symposium 2025 |
Web and mobile security
mobile security |
0.8 | 1 | 2024 | Wear's my Data? Understanding the Cross-Device Runtime Permission Model in Wearables · SP 2024 |
Authentication and access control › access control
permission analysis |
0.8 | 1 | 2024 | Wear's my Data? Understanding the Cross-Device Runtime Permission Model in Wearables · SP 2024 |
Authentication and access control › access control
permission management |
0.8 | 1 | 2024 | Wear's my Data? Understanding the Cross-Device Runtime Permission Model in Wearables · SP 2024 |
Methods — techniques the papers use, named apart from their topics
automated attack discovery · 1.7user study · 0.8taint analysis · 0.8
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Automated Discovery of Semantic Attacks in Multi-Robot Navigation Systems
Doguhan Yeke, Kartik Anand Pant, Muslum Ozgur Ozmen, Hyungsub Kim, James Goppert, Inseok Hwang 0002, Antonio Bianchi, Z. Berkay Celik |
USENIX Security Symposium | 1 |
| 2024 | Wear's my Data? Understanding the Cross-Device Runtime Permission Model in WearablesabstractWearable devices are becoming increasingly important, helping us stay healthy and connected. There are a variety of app-based wearable platforms that can be used to manage these devices. The apps on wearable devices often work with a companion app on users’ smartphones. The wearable device and the smartphone typically use two separate permission models that work synchronously to protect sensitive data. However, this design creates an opaque view of the management of permission-protected data, resulting in over-privileged data access without the user’s explicit consent. In this paper, we performed the first systematic analysis of the interaction between the Android and Wear OS permission models. Our analysis is two-fold. First, through taint analysis, we showed that cross-device flows of permission-protected data happen in the wild, demonstrating that 28 apps (out of the 150 we studied) on Google Play have sensitive data flows between the wearable app and its companion app. We found that these data flows occur without the users’ explicit consent, introducing the risk of violating user expectations. Second, we conducted an in-lab user study to assess users’ understanding of permissions when subject to cross-device communication (n = 63). We found that 66.7% of the users are unaware of the possibility of cross-device sensitive data flows, which impairs their understanding of permissions in the context of wearable devices and puts their sensitive data at risk. We also showed that users are vulnerable to a new class of attacks that we call cross-device permission phishing attacks on wearable devices. Lastly, we performed a preliminary study on other watch platforms (i.e., Apple’s watchOS, Fitbit, Garmin OS) and found that all these platforms suffer from similar privacy issues. As countermeasures for the potential privacy violations in cross-device apps, we suggest improvements in the system prompts and the permission model to enable users to make better-informed decisions, as well as on app markets to identify malicious cross-device data flows. Doguhan Yeke, Muhammad Ibrahim 0004, Güliz Seray Tuncay, Habiba Farrukh, Abdullah Imran, Antonio Bianchi, Z. Berkay Celik |
SP | 1 |