Demo Data from VLDB 2026 / reviewers in the wild / expert
Matthijs Schotsman
dblp:350/2370
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2026
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 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
1 paper |
Privacy and data protection · 100% | |
| Computer networks
1 paper |
Vehicular, aerial and satellite networks · 77% Wireless networking · 23% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Privacy and data protection
differential privacy |
1.0 | 1 | 2026 | Obfuscated Location Disclosure for Remote ID Enabled Drones · IEEE Trans. Dependable Secur. Comput. 2026 |
Privacy and data protection
location privacy |
1.0 | 1 | 2026 | Obfuscated Location Disclosure for Remote ID Enabled Drones · IEEE Trans. Dependable Secur. Comput. 2026 |
Wireless networking
broadcast |
0.3 | 1 | 2026 | Obfuscated Location Disclosure for Remote ID Enabled Drones · IEEE Trans. Dependable Secur. Comput. 2026 |
Methods — techniques the papers use, named apart from their topics
encryption · 2.0differential privacy · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Obfuscated Location Disclosure for Remote ID Enabled DronesabstractThe Remote ID (RID) regulation recently introduced by several aviation authorities worldwide (including the US and EU) forces commercial drones to regularly (max. every second) broadcast plain-text messages on the wireless channel, providing information about the drone identifier and current location, among others. Although these regulations increase the accountability of drone operations and improve traffic management, they allow malicious users to track drones via the disclosed information, possibly leading to drone capture and severe privacy leaks. In this paper, we propose Obfuscated Location disclOsure for RID-enabled drones (OLO-RID), a solution modifying and extending the RID regulation while preserving drones' location privacy. Rather than disclosing the actual drone's location, drones equipped with OLO-RID disclose a differentially private obfuscated location. OLO-RID also extends RID messages with encrypted location information, accessible only by authorized entities and valuable to obtain the current drone's location in safety-critical use cases. We design, implement, and deploy OLO-RID on a Raspberry Pi 3 and release the code of our implementation as open-source. We also perform an extensive performance assessment of the runtime overhead of our solution in terms of processing, communication, memory, and energy consumption. We show that OLO-RID can generate RID messages on a constrained device in less than 0.16 s while also requiring a minimal energy toll on a relevant device ($0.0236\%$of energy for a DJI Mini 2). We also evaluate the utility of the proposed approach in the context of three reference use cases involving the drones' location usage, demonstrating minimal performance degradation when trading off location privacy and utility for next-generation RID-compliant drone ecosystems. Alessandro Brighente, Mauro Conti, Matthijs Schotsman, Savio Sciancalepore |
IEEE Trans. Dependable Secur. Comput. | 3 |