Transcript Empirical Analysis of Crypto Currencies Manoj kumar popuri Cs 765

 Review
 Methodology
– Dataset
– Data Cleaning
– Technology
– Analysis
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Degree Distribution
Hubs
Top 100
Evolution
Anonymous Users
 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
 Review
 Methodology
– Dataset
– Data Cleaning
– Technology
– Analysis
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•
•
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
 Review
 Methodology
– Dataset
– Data Cleaning
– Technology
– Analysis
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Degree Distribution
Hubs
Top 100
Evolution
Anonymous Users
Technology
 Stanford Network Analysis Platform (SNAP)
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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.
Mixed coins
Coins
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Reserve
Bitmixer
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