That’s AmorE: Amortized Efficiency for Pairing Delegation
Paper i proceeding, 2025

Over two decades since their introduction in 2005, all major verifiable pairing delegation protocols for public inputs have been designed to ensure unconditional security. However, we note that a delegation protocol involving only ephemeral secret keys in the public view can achieve everlasting security, provided the server is unable to produce a pairing forgery within the protocol’s execution time. Thus, computationally bounding the adversary’s capabilities during the protocol’s execution may be more reasonable when the goal is to achieve significant efficiency gains for the delegating party. This consideration is particularly relevant given the continuously evolving computational costs associated with pairing computations and their ancillary blocks, which creates an ever-changing landscape for what constitutes efficiency in pairing delegation protocols. With the goal of fulfilling both efficiency and everlasting security, we present AmorE, a protocol equipped with an adjustable security and efficiency parameter for sequential pairing delegation, which achieves state-of-the-art Amortized Efficiency in terms of the number of pairing computations. For example, delegating batches of 10 pairings on the BLS48-575 elliptic curve via our protocol costs to the client, on average, less than a single scalar multiplication in G2 per delegated pairing, while still ensuring at least 40 bits of statistical security.

Författare

Adrian Perez Keilty

Göteborgs universitet

Chalmers, Data- och informationsteknik, Informationssäkerhet

Diego F. Aranha

Aarhus Universitet

Elena Pagnin

Chalmers, Data- och informationsteknik, Informationssäkerhet

Göteborgs universitet

Francisco Rodríguez-Henríquez

Technology Innovation Institute

Lecture Notes in Computer Science

0302-9743 (ISSN) 1611-3349 (eISSN)

Vol. 16007 LNCS 211-246
9783032019127 (ISBN)

45th Annual International Cryptology Conference, CRYPTO 2025
Santa Barbara, USA,

Ämneskategorier (SSIF 2025)

Datavetenskap (datalogi)

DOI

10.1007/978-3-032-01913-4_7

Mer information

Senast uppdaterat

2025-09-04