[Paper Review] Decentralized Identity in Practice: Benchmarking Latency, Cost, and Privacy

Abylay Satybaldy, Kamil Tylinski+1 more|arXiv (Cornell University)|Jan 28, 2026

Scientific Computing and Data Management0 citations

TL;DR

The paper benchmarks three ledger-based Decentralized Identifier (DID) methods (Ethereum, Hedera, XRPL) under a unified setup, assessing latency, cost, and privacy via a Metadata-Leakage Score (MLS).

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.

Motivation & Objective

• Motivate the need for cross-platform evidence on operational DID behavior.
• Benchmark latency, transaction cost, and on-chain metadata exposure across ledger-based DIDs.
• Normalize measurements to account for platform-specific intervals and fees.
• Quantify privacy leakage using an entropy-based MLS metric.
• Provide guidance for selecting and configuring DID systems under performance and privacy constraints.

Proposed method

• Use reference SDKs to implement DID operations on Ethereum, Hedera, and XRPL under a unified experimental setup.
• Measure latency, normalizing by each platform’s block or consensus interval.
• Measure cost, normalizing by the native value transfer fee.
• Quantify on-chain metadata exposure using a Metadata-Leakage Score (MLS) based on entropy per operation.
• Analyze how ledger architecture and SDK workflows influence latency, cost, and privacy.

Experimental results

Research questions

• RQ1 How do latency, cost, and privacy compare across Ethereum, Hedera, and XRPL for DID operations under a unified workflow?
• RQ2 What are the trade-offs between near-instant off-chain DID creation and on-chain lifecycle costs (Ethereum) versus deterministic latency and low fees (XRPL) and low-MLS (Hedera)?
• RQ3 How do SDK processing and confirmation pipelines affect observed performance and metadata leakage?
• RQ4 To what extent do ledger architecture and SDK workflows shape privacy exposure beyond the consensus mechanism?

Key findings

• Ethereum enables near-instant off-chain DID creation but has higher latency and cost for on-chain lifecycle operations.
• XRPL delivers deterministic, stable latency with fixed, low fees but higher metadata leakage due to verbose transaction payloads.
• Hedera achieves the lowest on-chain latency and low fees with minimal metadata leakage, with occasional variance from SDK processing.
• Ledger architecture and SDK workflows significantly influence latency, cost, and metadata exposure beyond consensus mechanisms.
• The findings support informed selection and configuration of DID systems under specific performance and privacy constraints.