Third SC6 webinar was held on 16 February 2017. It was organised by the Consortium of Social Science Data Archives (CESSDA) from Norway and the Semantic Web Company (SWC) from Austria. Theme of the webinar was “Insight into Virtual Currency Ecosystems” presented by Dr. Bernhard Haslhofer, Data Scientist at the Austrian Institute of Technology.

1. Insight into Virtual Currency Ecosystems
(by making use of Big Data technology)
Dr. Bernhard Haslhofer, Austrian Institute of Technology (AIT)
BDE SC6 Webinar, 2017-02-16

2. About me
• Data Scientist @ Austrian Institute of
Technology / Digital Insight Lab
• Research Interest: gain insight
from large, connected datasets
using machine learning, network
analytics and text mining methods
• Current focus: virtual currency
analytics
• Project(s): GraphSense
2
http://www.graphsense.info http://bernhardhaslhofer.info

3. Plan for today
• What are Virtual Currency Ecosystems?
• GraphSense | Goals, Features and Demo
• GraphSense | Technical Aspects
• Outlook and Challenges
3

4. 4
What are Virtual Currency Ecosystems?

5. Virtual Currency
• “A type of unregulated, digital money, which is issued and
usually controlled by its developers, and used and accepted
among the members of a specific virtual community.” (ECB)
• Functions: measure of value, medium of exchange, store of
value
• Currency codes: XBT, ETH, XMR, ….
• Currency symbols: B⃦, Ξ, ɱ, …
• Exchange rates to other currencies (USD, EUR, …)
5

6. Virtual Currency
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Centralized Decentralized
Regulated
E-money
Bank money (deposit)
Unregulated
Internet coupon
Mobile coupon
Centralized virtual
currency
Cryptocurrencies
(e.g., Bitcoin)
Non-Cryptocurrency
(e.g., Ripple, Stellar)
based on https://en.wikipedia.org/wiki/Virtual_currency

7. • Difference to other currency systems:
• No pre-assumed identities
• No central authority, no trusted third parties
• collective transaction management (blockchain)
• collective money issuance (mining)
Cryptocurrency
7

8. 8 Source: http://blockchain.info
647 Currencies

9. How do I make a
Bitcoin transaction?
9

10. 10

11. P2P Network
Broadcast
Transaction
Blockchain
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12. P2P Network
Blockchain
Miners
Collect pending
Transactions
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13. P2P Network
Blockchain
Miners
Find a block
13

14. P2P Network
Blockchain
Miners
Broadcast new
block
14
Miners

15. P2P Network
Synchronize
Blocks
Blockchain
Receive
Confirmations
15

16. P2P Network
Synchronize
Blocks
Blockchain
Receive
Confirmations
16

17. How do I get Bitcoins?
17

18. 18
Exchange

19. 19
2653 Markets

20. 20
Source: https://coinfinity.co/bitcoin-kaufen/
Bitcoin
ATMs
Bitcoin
Voucher
Service

21. Who accepts
Bitcoins?
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22. 22
Merchants

23. Merchants

24. 24
Payment
Providers

25. 25
Gambling
Sites

26. 26
Darknet
Marketplaces

27. 27
Mixing
Services

28. Virtual Currency Ecosystem
28
Gambling
Sites
Miners
Mixing
Services
Darknet
Marketplaces
Merchants
Payment
ProvidersBitcoin
ATMs
Bitcoin
Voucher
Service
Exchange

29. 29
GraphSense | Goal, Features, and Demo

30. Goals and Features
• Provide insight into Virtual Currency Ecosystems
• Microscopic view: inspect atomic entities (block,
transaction, address, currency flows)
• Macroscopic view: investigate real-world actors
(exchanges, payment services, etc.) and the
currency flows between them
30

31. Approach
31
A
A A
AA
C
T
BlockchainAddress
Graph
Address
Cluster
Tags
Enrichmentprocess

32. 32

33. 33
GraphSense | Technical Aspects

34. Overall Architecture
34

35. Data Processing (v.0.2.1)
35

36. Data Processing (v.0.3)
36

37. 37

38. Cross-ledger Analytics
38
L$
OM¢

39. Challenge #1: Volume
• At the moment we only
process Bitcoin transactions
• raw data: 91 GB
• transformed: 217 GB
• DB (with indices): 757 GB
• There are at least 646 other
virtual currencies
39

40. Challenge #2: Variety
• Virtual currencies differ in their conceptual design
• Protocols change over time
• Need: flexible, horizontally scalable data storage
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41. Challenge #3: Velocity
• Bitcoin blocks
• limited to 1MB (1000 - 2000 transactions)
• interval between blocks: ~10min
• block size will most likely grow in future
• Other currencies implement higher frequencies
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42. • GraphSense address graph
• ~ 212 million addresses (nodes)
• ~ 1.36 billion flows between addresses (edges)
• We need graph algorithms that
• compute connected components efficiently on large graphs
• leverage distributed computing paradigms (map-reduce)
• also work for large graphs with a skewed node degree
distribution
42
Challenge #4: Large Graphs

43. 43
Information & Contacts:
CESSDA, Ivana Ilijasic Versic, ivana.versic@cessda.net
Semantic Web Company, Martin Kaltenböck, m.kaltenboeck@semantic-web.at
Austrian Institute for Technology, Bernhard Haslhofer, Bernhard.Haslhofer@ait.ac.at
Big Data Europe – Information & Outlook
BDE website: http://www.big-data-europe.eu
Mailing List: http://eepurl.com/bg3vCr
Big Data Integrator Platform (BDI): https://www.big-data-europe.eu/platform/
WATCH OUT: 3.5.2017 – Final BDI Release
16-févr.-17www.big-data-europe.eu