EMPIRICAL ANALYSIS OF CRYPTO CURRENCIES MANOJ KUMAR POPURI

CS 765

OUTLINE

Review Methodology

– Dataset– Data Cleaning – Technology– Analysis

• Degree Distribution• Hubs• Top 100• Evolution• Anonymous Users

Review

Crypto Currencies are the subset of digital currencies where cryptography is used to secure the transactions and creation of new units.

There are 530 crypto currencies in Market, with total Market Cap: $ 5,588,693,508 / 24h Vol: $ 47,885,015 .

Bitcoin , Litecoin, Namecoin, Ripple, Dogecoin, and Darkcoin.

Review- Transaction

OUTLINE

Review Methodology

– Dataset– Data Cleaning – Technology– Analysis

• Degree Distribution• Hubs• Top 100• Evolution• Anonymous Users

Dataset

When you install a crypto currency wallet it will synchronize with all its previous transactions.

Data Cleaning

Decrypt the .dat file. I have written scripts to clean the data into Node- Coin Address Edges:- The in and out transactions of the

Addresses. Files Generated:

– Addresses.txt– tx.txt– Txin.txt– Txout.txt– Txtime.txt

OUTLINE

Review Methodology

– Dataset– Data Cleaning – Technology– Analysis

• Degree Distribution• Hubs• Top 100• Evolution• Anonymous Users

Technology

Stanford Network Analysis Platform (SNAP)  It easily scales to massive networks with hundreds of

millions of nodes, and billions of edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

Massively scalable up to several billion entities Distributed across multiple machines Graph query language (Cypher) Optimized, high speed traversal framework Embeddable REST interface and an API

Technology - Gephi

Analysis

Degree Distribution Correlation of user activity and the number of

transactions to the exchange rate. To find the Mixers/ Money laundering nodes.

The richest people. ( Top 100 nodes in each network ) Evolution of the network with time.

Dark side of crypto currencies ( To find the percentage of users accessing the network using anonymisers like tor network ).

Degree Distribution

The Degree distributions in the crypto currency networks point out the growth of the network over time.

The degree distribution can be constructed by calculating the degree k for each user entity For every year since the start of all the currencies by counting and summing incoming and outgoing transactions.

Money Laundering Nodes

Although the identity of the users is anonymous in the crypto currencies, visible balance and ID information as a basis from which to track users future transactions or to study previous activity.

This makes users to attract towards the mixers. Mixers receives currency from various users and mix the currency, also takes care in not transferring the same coin to the user.

» BitmixerCoins

Reserve

Mixed coins

Mixers

Top 100 Nodes

After seeing some interesting characteristics in the top 100 richest nodes in each network I wanted to analyze the behavior of the richest people in each network.

We crawled the rich node list from Bitinfocharts.

Litecoin Rich list

Bitcoin Richlist

Anonymisers in Crypto Currencys

We want to analyze the percentage of users accessing the bitcoin network using anonymisers like tor.

To do this we crawled the data from blockchain.info for the IP addresses of the users connected to the bitcoin network and the list of exit nodes from tor.

And compared both lists for match.

Anonymisers in Crypto Currencies

Python Scrypt to Automatically crawl both the websites and compare the IP addresses, Lists the matched addresses along with the location into the text file labeled with time.

Summary

We are analyzing some of the crypto currency networks to find the degree distributions, and the evolution of the network with time.

Analyzing the Hubs in the networks to find the Money mixers in the network.

Analyzing the characteristics of the rich people in each network.

Finding the percentage of users accessing the bitcoin network anonymously.

Questions