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Bitcoin Burn Addresses: Unveiling the Permanent Losses and Their Underlying Causes

Mohamed El Khatib, Arnaud Legout

TL;DR

This work tackles the challenge of identifying Bitcoin burn addresses and quantifying permanent losses, by constructing a ground-truth labeled set and training a multilayer perceptron on a Base60-like encoding that preserves character and position information. Through an iterative reinforcement process, the authors assemble the largest burn-address catalog to date (7,905 addresses) and quantify losses as 3,197.61 BTC (about $295M USD in Nov 2024), representing $0.016\%$ of the total supply, with a heavy concentration in the top three addresses. The study reveals diverse burn-address usages, including proof-of-burn token creation, message storage via Base58, image storage via Bech32/OLGA Stamps, and protocol operations, while publicly releasing datasets, code, and the trained model. These contributions enable ongoing monitoring of permanent Bitcoin losses and enrich understanding of blockchain data persistence practices and novel protocol mechanisms.

Abstract

Bitcoin burn addresses are addresses where bitcoins can be sent but never retrieved, resulting in the permanent loss of those coins. Given Bitcoin's fixed supply of 21 million coins, understanding the usage and the amount of bitcoins lost in burn addresses is crucial for evaluating their economic impact. However, identifying burn addresses is challenging due to the lack of standardized format or convention. In this paper, we propose a novel methodology for the automatic detection of burn addresses using a multi-layer perceptron model trained on a manually classified dataset of 196,088 regular addresses and 2,082 burn addresses. Our model identified 7,905 true burn addresses from a pool of 1,283,997,050 addresses with only 1,767 false positive. We determined that 3,197.61 bitcoins have been permanently lost, representing only 0.016% of the total supply, yet 295 million USD on November 2024. More than 99% of the lost bitcoins are concentrated in just three addresses. This skewness highlights diverse uses of burn addresses, including token creation via proof-of-burn, storage of plain text messages, or storage of images using the OLGA Stamps protocol.

Bitcoin Burn Addresses: Unveiling the Permanent Losses and Their Underlying Causes

TL;DR

This work tackles the challenge of identifying Bitcoin burn addresses and quantifying permanent losses, by constructing a ground-truth labeled set and training a multilayer perceptron on a Base60-like encoding that preserves character and position information. Through an iterative reinforcement process, the authors assemble the largest burn-address catalog to date (7,905 addresses) and quantify losses as 3,197.61 BTC (about 0.016\%$ of the total supply, with a heavy concentration in the top three addresses. The study reveals diverse burn-address usages, including proof-of-burn token creation, message storage via Base58, image storage via Bech32/OLGA Stamps, and protocol operations, while publicly releasing datasets, code, and the trained model. These contributions enable ongoing monitoring of permanent Bitcoin losses and enrich understanding of blockchain data persistence practices and novel protocol mechanisms.

Abstract

Bitcoin burn addresses are addresses where bitcoins can be sent but never retrieved, resulting in the permanent loss of those coins. Given Bitcoin's fixed supply of 21 million coins, understanding the usage and the amount of bitcoins lost in burn addresses is crucial for evaluating their economic impact. However, identifying burn addresses is challenging due to the lack of standardized format or convention. In this paper, we propose a novel methodology for the automatic detection of burn addresses using a multi-layer perceptron model trained on a manually classified dataset of 196,088 regular addresses and 2,082 burn addresses. Our model identified 7,905 true burn addresses from a pool of 1,283,997,050 addresses with only 1,767 false positive. We determined that 3,197.61 bitcoins have been permanently lost, representing only 0.016% of the total supply, yet 295 million USD on November 2024. More than 99% of the lost bitcoins are concentrated in just three addresses. This skewness highlights diverse uses of burn addresses, including token creation via proof-of-burn, storage of plain text messages, or storage of images using the OLGA Stamps protocol.

Paper Structure

This paper contains 37 sections, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Background
  4. Address encoding
  5. Burn addresses
  6. Vanity addresses
  7. Initial training set construction
  8. Feature selection and encoding
  9. Model training
  10. Training, prediction, and reinforcement
  11. Model limitations and area of improvements
  12. Publicly available material
  13. Results
  14. Model predictions
  15. Amount of Bitcoin lost in burn addresses
  16. ...and 22 more sections

Figures (2)

  • Figure 1: Histogram of the Shannon entropy for all Bitcoin addresses. The y-axis is log scale.
  • Figure 2: Distribution of the first apparition date of each burn address.