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Ben Lampert

dblp:187/5423 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Cryptographic primitives and cryptanalysis
random number generation
0.212016
Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016
Cryptographic primitives and cryptanalysis › random number generation
true random number generator
0.212016
Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016
Embedded and real-time systems
embedded system security
0.212016
Robust, low-cost, auditable random number generation for embedded system security · SenSys 2016
Cryptographic primitives and cryptanalysis › random number generation
entropy source
0.112016
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
YearPublicationVenuePosition
2016 Robust, low-cost, auditable random number generation for embedded system security
abstract
This 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
SenSys1