Demo Data from EDBT 2026 / reviewers in the wild / expert
Ben Lampert
dblp:187/5423
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2016
—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.
| Network and information security
1 paper |
Cryptographic primitives and cryptanalysis · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Embedded and real-time systems · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Cryptographic primitives and cryptanalysis
random number generation |
0.2 | 1 | 2016 | Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016 |
Cryptographic primitives and cryptanalysis › random number generation
true random number generator |
0.2 | 1 | 2016 | Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016 |
Embedded and real-time systems
embedded system security |
0.2 | 1 | 2016 | Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016 |
Cryptographic primitives and cryptanalysis › random number generation
entropy source |
0.1 | 1 | 2016 | Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016 |
Methods — techniques the papers use, named apart from their topics
avalanche noise · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2016 | Robust, low-cost, auditable random number generation for embedded system securityabstractThis paper presents an architecture for a discrete, high-entropy hardware random number generator. Because it is constructed out of simple hardware components, its operation is transparent and auditable. Using avalanche noise, a non-deterministic physical phenomenon, the circuit is inherently probabilistic and resists adversarial control. Furthermore, because it compares the outputs from two matched noise sources, it rejects environmental disturbances like RF energy and power supply ripple. The resulting hardware produces more than 0.98 bits of entropy per sample, is inexpensive, has a small footprint, and can be disabled to conserve power when not in use. Ben Lampert, Riad S. Wahby, Shane Leonard, Philip Alexander Levis |
SenSys | 1 |