Decentralized Identity in Practice: Benchmarking Latency, Cost, and Privacy

TL;DR

This study empirically benchmarks three ledger-based DID methods—Ethereum, Hedera, and XRP Ledger—evaluating their latency, cost, and privacy leakage to inform better system selection and configuration.

Contribution

It provides a systematic comparison of DID implementations across different ledgers, highlighting architectural trade-offs and operational behaviors.

Findings

Ethereum has high latency and cost for on-chain DID operations.

XRPL offers low latency and fees but higher metadata leakage.

Hedera achieves low latency, low fees, and minimal metadata exposure.

Abstract

Decentralized Identifiers (DIDs) are increasingly deployed on distributed ledgers, yet systematic cross-platform evidence on their operational behavior remains limited. We present an empirical benchmarking study of three prominent ledger-based DID methods - Ethereum, Hedera, and XRP Ledger - using reference Software Development Kits (SDKs) under a unified experimental setup. We measure latency, transaction cost, and on-chain metadata exposure, normalizing latency by each platform's block or consensus interval and cost by its native value transfer fee. Privacy leakage is quantified using a Metadata-Leakage Score (MLS), an entropy-based measure expressed in bits per operation. Our results reveal distinct architectural trade-offs. Ethereum enables near-instant, off-chain DID creation, but incurs the highest latency and cost for on-chain lifecycle operations. XRPL delivers deterministic and stable latency with fixed, low fees, yet exhibits higher metadata leakage due to more verbose transaction payloads. Hedera achieves the lowest on-chain latency and low fees with minimal metadata leakage, while occasional variance arises from SDK-side processing and confirmation pipelines. Overall, the findings show that ledger architecture and SDK workflows play a major role in shaping DID latency, cost, and metadata exposure, complementing the effects of the underlying consensus mechanism. These results provide evidence-based insights to support informed selection and configuration of DID systems under performance and privacy constraints.