Legitimate Browser Cryptocurrency Mining - Feasibility Study and Analysis

Table of Contents

1. Introduction

Browser-based cryptocurrency mining emerged as a potential alternative monetization model for web content, with Coinhive's JavaScript miner launching in September 2017. Despite initial promise, the technology gained notoriety through cryptojacking - unauthorized mining on unsuspecting users' devices. This study examines the feasibility of legitimate browser mining with proper user consent mechanisms.

2. Methodology

The research employed an experimental online blog (hippocrypto.me) using Coinhive to mine Monero cryptocurrency with 107 volunteer participants aged 18-55.

2.1 Experimental Setup

The study compared browser mining against traditional display advertising, measuring user preferences, revenue generation, and user experience across desktop and mobile platforms.

2.2 User Study Design

Participants were presented with both monetization methods and surveyed about their preferences, with particular attention to consent mechanisms and user-adjustable hash rates.

3. Technical Framework

Browser mining leverages JavaScript to perform cryptographic hashing directly in web browsers, utilizing visitor computational resources.

3.1 Cryptocurrency Mining Algorithms

The study focused on Monero (XMR) mining using the CryptoNight algorithm, selected for its ASIC-resistant properties and suitability for CPU mining.

3.2 Browser Mining Implementation

Coinhive's JavaScript library was implemented with the AuthedMine variant requiring explicit user consent before initiating mining operations.

4. Results and Analysis

The study revealed significant insights about user acceptance and economic viability of browser mining.

4.1 Revenue Comparison

Browser mining currently generates revenue at a rate 46 times less than traditional advertising. However, this gap is expected to decrease with ASIC-resistant mining implementations.

4.2 User Experience Metrics

Over 60% of participants preferred browser mining over advertising when they received half of the mined cryptocurrency, indicating the importance of user investment in the ecosystem.

5. Original Analysis

The feasibility study by Venskutonis et al. represents a crucial examination of browser-based cryptocurrency mining as a legitimate monetization alternative. Their research demonstrates that when implemented ethically with proper consent mechanisms, browser mining can provide a viable alternative to traditional advertising models. The finding that 60% of users prefer mining over ads when receiving half the cryptocurrency echoes principles from behavioral economics, where user participation and ownership dramatically increase acceptance rates.

Technically, the choice of Monero for this study aligns with its ASIC-resistant properties, making it more suitable for browser-based CPU mining compared to Bitcoin's SHA-256 algorithm. This approach mirrors the philosophy behind privacy-focused cryptocurrencies that prioritize decentralization and accessibility. The revenue gap of 46 times compared to traditional advertising, while substantial, must be contextualized within the evolving cryptocurrency market and improving mining efficiencies.

From a user experience perspective, the study's emphasis on adjustable hash rates reflects important human-computer interaction principles. Similar to progressive web app design patterns that prioritize user control, this approach acknowledges the need for transparency in resource utilization. The research contributes to the broader discussion about web monetization alternatives, particularly relevant as ad-blocker usage continues to grow at 11% annually according to PageFair's 2023 Ad Blocking Report.

Compared to other alternative monetization models like Brave's Basic Attention Token or Web Monetization API, browser mining offers a more direct cryptographic foundation. However, challenges remain in energy efficiency and mobile device performance. Future developments in WebAssembly and improved JavaScript engines could significantly enhance mining efficiency, potentially closing the revenue gap with traditional advertising.

6. Technical Implementation

6.1 Mathematical Foundation

Cryptocurrency mining involves solving cryptographic puzzles through proof-of-work. The mining difficulty adjusts based on network hashrate:

$Difficulty = \frac{Target}{2^{208}}$

The expected time to find a block can be calculated as:

$E[T] = \frac{D \cdot 2^{48}}{65535 \cdot H}$

where $D$ is difficulty and $H$ is hashrate.

6.2 Code Implementation

Basic Coinhive mining implementation with user consent:

// Initialize AuthedMine with explicit user consent
if (userConsentGranted) {
    var miner = new CoinHive.Anonymous('SITE_KEY', {
        throttle: 0.5, // User-adjustable throttle
        threads: 2     // Adjustable thread count
    });
    
    // Start mining only after consent
    miner.start();
    
    // Revenue sharing implementation
    miner.on('found', function() {
        allocateUserReward(0.5); // 50% to user
    });
}

7. Future Applications

Browser mining technology has potential applications beyond website monetization:

• Microtransaction Systems: Pay-per-content access without subscription fees
• Progressive Web Apps: Alternative revenue models for PWAs
• Educational Platforms: Mining while learning about blockchain technology
• Decentralized Content Networks: Integrated mining in peer-to-peer networks

Future developments could include improved energy efficiency through WebAssembly optimization, mobile-specific mining algorithms, and integration with emerging web standards like the Web Monetization API.

8. References

1. Venskutonis, S., Hao, F., & Collison, M. (2018). On legitimate mining of cryptocurrency in the browser – a feasibility study. arXiv:1812.04054
2. Narayanan, A., et al. (2016). Bitcoin and Cryptocurrency Technologies. Princeton University Press.
3. Coinhive Documentation. (2017). JavaScript Mining Library.
4. PageFair. (2023). Ad Blocking Report: Global Usage Statistics.
5. Monero Project. (2023). CryptoNight Algorithm Specification.
6. Zhu, J.Y., et al. (2017). Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks. ICCV.
7. W3C Web Monetization Working Group. (2023). Web Monetization API Specification.