2.  Review  Methodology –Dataset –Data Cleaning –Technology –Analysis Degree Distribution Hubs Top 100 Evolution Anonymous Users

3.  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.

4. Review- Transaction

5.  Review  Methodology –Dataset –Data Cleaning –Technology –Analysis Degree Distribution Hubs Top 100 Evolution Anonymous Users

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

7. 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

8.  Review  Methodology –Dataset –Data Cleaning –Technology –Analysis Degree Distribution Hubs Top 100 Evolution Anonymous Users

9. 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

10. Technology - Gephi

11. 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 ).

12. 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.

13. 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. » BitmixerBitmixer Coins Reserve Mixed coins

14. Mixers

15. 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.Bitinfocharts

16. Litecoin Rich list

17. Bitcoin Richlist

18. 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.

20. 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.

21. 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.

22. Questions