Adapting Ephemeral ECC Authentication for Decentralized Web 3.0: A Threat Model and Security Analysis
Abstract
Web 3.0 shifts control of digital identity and data away from centralized platforms, moving authentication onto resource-constrained end-user devices, wallets, and smart contracts. RSA and ElGamal are computationally expensive at the key sizes required for adequate security, making them a poor match for this setting. This study presents a user-centric Web 3.0 authentication framework combining Elliptic Curve Cryptography with an ephemeral Elliptic Curve ElGamal (ECEG) character-encoding scheme, building on an ephemeral-encoding design for constrained IoT devices and its subsequent hash/XOR authentication extension. Unlike that prior design, the mutual-authentication step here binds each party to its long-term key through digital signatures and replaces static-key ECEG transport with a two-ephemeral-key ECEG transport, providing an erasure-conditional forward-secrecy property that the prior design lacks. We define a Dolev-Yao threat model with an in-scope/out-of-scope adversary boundary, an assumption-based security analysis, a formal-verification specification, and a decentralized-identifier/verifiable-credential mechanism binding long-term public keys to claimed identities, addressing the identity-binding gap that otherwise limits man-in-the-middle resistance without a common certificate authority. A protocol-level operation count and a complexity comparison against RSA are reported without claiming empirical superiority. We situate the framework against its closest prior IoT-oriented work and active 2025–2026 literature on ECC- and blockchain-based decentralized authentication. Recent 2026 analyses have reduced estimates of the quantum resources needed to solve the elliptic-curve discrete logarithm problem underlying secp256k1-style curves; we give a post-quantum outlook against current NIST migration guidance. The evaluation remains analytical rather than empirical; formal verification and hardware benchmarking remain necessary before production deployment.