Demo Data from EDBT 2026 / reviewers in the wild / expert
Andre Kalamandeen
dblp:18/8210
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Computer networks · 1 · 1 first-author
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.
| Computer networks
1 paper |
Wireless sensing and localization · 100% | |
| Network and information security
1 paper |
Authentication and access control · 100% | |
| Human-computer interaction and pervasive computing
1 paper |
Ubiquitous computing and smart environments · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Wireless sensing and localization
proximity detection |
0.1 | 1 | 2010 | Ensemble: cooperative proximity-based authentication · MobiSys 2010 |
Wireless sensing and localization
received signal strength |
0.1 | 1 | 2010 | Ensemble: cooperative proximity-based authentication · MobiSys 2010 |
Authentication and access control
proximity-based authentication |
0.1 | 1 | 2010 | Ensemble: cooperative proximity-based authentication · MobiSys 2010 |
Ubiquitous computing and smart environments
device pairing |
0.0 | 1 | 2010 | Ensemble: cooperative proximity-based authentication · MobiSys 2010 |
Methods — techniques the papers use, named apart from their topics
diffie-hellman key exchange · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Ensemble: cooperative proximity-based authenticationabstractEnsemble is a system that uses a collection of trusted personal devices to provide proximity-based authentication in pervasive environments. Users are able to securely pair their personal devices with previously unknown devices by simply placing them close to each other (e.g., users can pair their phones by just bringing them into proximity). Ensemble leverages a user's growing collection of trusted devices, such as phones, music players, computers and personal sensors to observe transmissions made by pairing devices. These devices analyze variations in received signal strength (RSS) in order to determine whether the pairing devices are in physical proximity to each other. We show that, while individual trusted devices can not properly distinguish proximity in all cases, a collection of trusted devices can do so reliably. Our Ensemble prototype extends Diffie-Hellman key exchange with proximity-based authentication. Our experiments show that an Ensemble-enabled collection of Nokia N800 Internet Tablets can detect devices in close proximity and can reliably detect attackers as close as two meters away. Andre Kalamandeen, Adin Scannell, Eyal de Lara, Anmol Sheth, Anthony LaMarca |
MobiSys | 1 |