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Rachel Mislovaty

dblp:15/4048 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1

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 protocols and secure computation · 50% Cryptographic primitives and cryptanalysis · 50%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Cryptographic protocols and secure computation
key exchange		 0.012004
Synchronization of neural networks by mutual learning and its application to cryptography · NIPS 2004
Cryptographic primitives and cryptanalysis
neural cryptography		 0.012004
Synchronization of neural networks by mutual learning and its application to cryptography · NIPS 2004

Methods — techniques the papers use, named apart from their topics

statistical physics · 0.0mutual learning · 0.0
YearPublicationVenuePosition
2004 Synchronization of neural networks by mutual learning and its application to cryptography
abstract
Two neural networks that are trained on their mutual output synchronize to an identical time dependant weight vector. This novel phenomenon can be used for creation of a secure cryptographic secret-key using a public channel. Several models for this cryptographic system have been suggested, and have been tested for their security under different sophis- ticated attack strategies. The most promising models are networks that involve chaos synchronization. The synchronization process of mutual learning is described analytically using statistical physics methods.
Einat Klein, Rachel Mislovaty, Ido Kanter, Andreas Ruttor, Wolfgang Kinzel
 NIPS2