B3DH: A Drop-In Optimization of Triple Diffie-Hellman Key Exchange Protocols Maintaining Tight Security Bound

The triple Diffie–Hellman (3DH) authenticated key exchange protocol is widely adopted in practice due to its simplicity and strong performance. It is employed, for instance, in the Noise framework, Signal, and WhatsApp. This protocol requires four exponentiations per endpoint and only a single round of communication, during which Alice and Bob each exchange a single group element, while providing a (weakened) form of forward secrecy. At CRYPTO 2019, Cohn-Gordon et al. provided a proof with optimal tightness for this protocol under the SDH assumption in the ROM, allowing for further performance improvements by choosing optimal security parameters. In this paper, we show that even greater efficiency can be achieved by introducing Batched-3DH (B3DH), a variant of 3DH that requires only three exponentiations, while admitting a security proof as tight as that of Cohn-Gordon et al. under the same assumptions. The only difference from 3DH lies in the computation of the initial shared key prior to key derivation; in particular, the long-term keys, as well as the interaction between Alice and Bob, remain unchanged, meaning that practical protocols built on 3DH can be readily adapted to B3DH. Finally, we implement B3DH and compare its efficiency with that of standard 3DH, showing that the practical performance gain closely matches the theoretical improvement of 25 percent when operations other than exponentiations are neglected.

Authors

Institutions

Publication Details

Journal
IACR Communications in Cryptology
Published
2026-10-26
DOI
https://doi.org/10.62056/ah5w4fbmo
Primary Topic
Cryptography and Data Security
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

B3DH: A Drop-In Optimization of Triple Diffie-Hellman Key Exchange Protocols Maintaining Tight Security Bound

Xavier Bultel, Khouredia Cisse
IACR Communications in Cryptology
Cryptography and Data Security
article

B3DH: A Drop-In Optimization of Triple Diffie-Hellman Key Exchange Protocols Maintaining Tight Security Bound

Xavier Bultel, Khouredia Cisse
article en

Abstract

The triple Diffie–Hellman (3DH) authenticated key exchange protocol is widely adopted in practice due to its simplicity and strong performance. It is employed, for instance, in the Noise framework, Signal, and WhatsApp. This protocol requires four exponentiations per endpoint and only a single round of communication, during which Alice and Bob each exchange a single group element, while providing a (weakened) form of forward secrecy. At CRYPTO 2019, Cohn-Gordon et al. provided a proof with optimal tightness for this protocol under the SDH assumption in the ROM, allowing for further performance improvements by choosing optimal security parameters. In this paper, we show that even greater efficiency can be achieved by introducing Batched-3DH (B3DH), a variant of 3DH that requires only three exponentiations, while admitting a security proof as tight as that of Cohn-Gordon et al. under the same assumptions. The only difference from 3DH lies in the computation of the initial shared key prior to key derivation; in particular, the long-term keys, as well as the interaction between Alice and Bob, remain unchanged, meaning that practical protocols built on 3DH can be readily adapted to B3DH. Finally, we implement B3DH and compare its efficiency with that of standard 3DH, showing that the practical performance gain closely matches the theoretical improvement of 25 percent when operations other than exponentiations are neglected.

IACR Communications in CryptologyVol. 3(3)
Université d'Orléans (FR), Services déconcentrés d'appui à la recherche Val de Loire (FR), Institut National des Sciences Appliquées Centre Val de Loire (FR)
Agence Nationale de la Recherche
Openalex Percentile: Top 14%
Cryptography and Data Security
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon