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O BITCOIN WHERE ART THOU?
An Introduction to Cryptocurrency Analytics
Dr. Bernhard Haslhofer
Oesterreichische Nationalbank (OeNB) | Research Seminar
2018-01-12
1. Which cryptocurrency should I buy?
2. What do you think about cryptocurrency X?
3. Where can I buy cryptocurrency X? Is it safe?
4. Can you help me setting up a cold wallet?
5. How will the price of cryptocurrency X evolve?
MOST FREQUENT QUESTIONS I GET
2
PUBLIC ATTENTION
3
• Cryptocurrencies have entered mainstream
• Public opinion is often misinformed and based
on insufficient and / or unexamined evidence
• Systematic scientific examination of entire
cryptocurrency ecosystems still in its infancy
• Missing methods, insufficient tool support
MOTIVATION
Image source: https://www.flickr.com/photos/namecoin/22995486509
4
• Contribute to a better understanding of the structure
and dynamics of cryptocurrency ecosystems
• Multidisciplinary cooperation to answer specific
(research) questions related to cryptocurrencies
• Develop scalable quantitative methods, tools and
services that help in answering those questions
• Micro-level analysis: inspect atomic entities
(block, transaction, address, currency flow)
• Macro-level analysis: investigate real-world actors
and services and their relationships
OUR GOALS
CRYPTOCURRENCY
ANALYTICS
5
INSIGHT INTO
CRYPTOCURRENCY
ECOSYSTEMS
Global
De-centralized
Transparent
Pseudo-Anonymous
Complex, dynamic
Networks
Exchanges
ATMs /
Vouchers
Payment
Services
Darknet
Markets
Mixing
Services
graphsense.info
30M
clusters
480K
blocks
1.5B
relations
296M
addresses
249M
transactions
For Whom?
Science
Public
Authorities
FinTech /
Banks
• Bitcoin: A (Very) Brief Introduction
• Cryptocurrency Analytics Methods
• GraphSense Cryptocurrency Analytics Platform
• Example Study: Ransomware Payments in the Bitcoin Ecosystem
• Future Research Directions
• Q & A
MY PLAN FOR TODAY
7
EXAMPLE TRANSACTION
8
TRANSACTION PROCESSING
Broadcast
Transaction
Blockchain
9
Bitcoin P2P Network
TRANSACTION PROCESSING
Collect pending
Transactions
Blockchain
10
Bitcoin Miners
Bitcoin P2P Network
TRANSACTION PROCESSING
Find & Broadcast
Block
Bitcoin P2P Network
Bitcoin Miners
Blockchain
11
TRANSACTION PROCESSING
Synchronize
Blocks
Blockchain
12
Bitcoin P2P Network
ANATOMY OF A BITCOIN TRANSACTION
13
txid: a6b06e...
blockhash: 0000ba7..
txid: 7f252a ….
vout: 1
scriptSig: Signature
value: 0.00460479
n: 0
addresses: [1Archive…]
value: 0.00566296
n: 1
addresses: [1MuSWq…]
List of inputs List of outputs
Bitcoin
Addresses
Reference to unspent
output of previous
transaction (UTXO)
ANATOMY OF A BITCOIN TRANSACTION
14
txid: a6b06e...
blockhash: 0000ba7..
txid: 7f252a ….
vout: 1
scriptSig: Signature
value: 0.00460479
n: 0
addresses: [1Archive…]
value: 0.00566296
n: 1
addresses: [1MuSWq…]
sum(List of inputs) sum(List of outputs)≥
ANATOMY OF A BITCOIN TRANSACTION
15
txid: a6b06e...
blockhash: 0000ba7..
txid: 7f252a ….
vout: 1
scriptSig: Signature
value: 0.00460479
n: 0
addresses: [1Archive…]
value: 0.00566296
n: 1
addresses: [1MuSWq…]
sum(List of inputs) sum(List of outputs)−Transaction Fee =
COINBASE TRANSACTION
16
txid: a60f6e2b...
blockhash: 0000ba7..
coinbase: “0367c4...” value: 25.42394247
n: 0
addresses: [1KFHE7…]
• Bitcoin: A (Very) Brief Introduction
• Cryptocurrency Analytics Methods
• GraphSense Cryptocurrency Analytics Platform
• Example Study: Ransomware Payments in the Bitcoin Ecosystem
• Future Research Directions
• Q & A
MY PLAN FOR TODAY
17
My focus for today
18
TAXONOMY OF ANALYTICS METHODS
Cryptocurrency
Analytics
P2P Network
Analytics
Blockchain Analytics
Network Analytics Clustering Heuristics
[Biryukov et al., 2014]
19
BLOCKCHAIN ANALYTICS
Blockchain Analytics
Network Analytics Clustering Heuristics
Transaction Network
Address Network
20
TRANSACTION NETWORK
t1
t3
t2
t4
[Reid and Harrigan 2012]
0,00321 BTC
2016-03-14 17:33:50
directed
acyclic
temporal
21
TRANSACTION NETWORK | CONSTRUCTION
txid: a6b06e...
blockhash: 0000ba7..
txid: 7f252a ….
vout: 1
scriptSig: Signature
vin
value: 0.00460479
n: 0
addresses: [1Archive…]
vout
Target node id
Source node id
Timestamp (via referenced block)
Value
22
ADDRESS NETWORK
a1
a2
a3
a5
a7 a8
a6
a9 a10
Estimated Value
No. Transactions
[Fleder et al. 2015, Filtz et al. 2017]
(bi-)directed
cyclic
23
ADDRESS NETWORK | CONSTRUCTION
txid: a6b06e...
blockhash: 0000ba7..
txid: 7f252a ….
vout: 1
scriptSig: Signature
vin
value: 0.00460479
n: 0
addresses: [1Archive…]
vout
Source node id
Target node id
No Transactions = aggregated count of edges with same node ids
24
ESTIMATED VALUE | COMPUTATION
inputs and outputs as shown in Table I.
Fig. 2. Bitcoin transaction value assignment
Therefore, we estimate the flow of actual Bitcoins betw
two addresses using the following formula:
TABLE I. BITCOINFLOW
Transaction Formula Estimated BTC
A1 A3 3 * (2/7) 0.857
A2 A3 3 * (5/7) 2.143
A1 A3 4 * (2/7) 1.143
A2 A4 4 * (5/7) 2.857
IV. ANALYSIS
a1 → a3 = 3	 ∗	
&
'
a1
tx
a2
a3
a4
2 3
45
[Filtz et al. 2017]
25
BLOCKCHAIN ANALYTICS
Blockchain Analytics
Network Analysis Clustering Heuristics
Multiple-Input Heuristics [Nakamoto, 2008]
Change Heuristics [Meiklejohn, 2013]
Temporal Behaviour [Ortega, 2013]
Transaction Fingerprinting [Fleder et. al, 2015]
c1
c1
MULTIPLE INPUT HEURISTICS
26
a1
tx
a2
c2
a2
ty
a3
Same address
[Nakamoto, 2008]
c1
MULTIPLE INPUT HEURISTICS
27
a1
tx
a2
a2
ty
a3
Tag
Tag
[Nakamoto, 2008]
28
BLOCKCHAIN ANALYTICS
Blockchain Analytics
Network Analysis Clustering Heuristics
c6c5
c4
c7
c3
c2
c1
29
CLUSTER / ENTITY NETWORK
a1
a2
a3
a5
a7 a8
a6
a9 a10
Estimated Value
No. Transactions
directed
cyclic
• Address clustering
• is a cornerstone of cryptocurrency analysis
• partitions the set of addresses observed in the Bitcoin blockchain into maximal
subsets of addresses that are likely controlled by the same real-world actor
• Multiple input heuristics can identify more than 69% of the addresses stored by
lightweight clients
MULTIPLE INPUT HEURISTICS | EFFECTIVENESS
30
[Harrigan and Fretter, 2016]
Figure 1. A graphical summary of the most significant flows of bitcoin between the largest address clusters during Bitcoin’s first five years in existence.
The vertices correspond to address clusters: red vertices are darknet markets; purple vertices are gambling services; green vertices are exchanges and blue
vertices are mining pools. The gray vertices are not immediately identifiable using publicly available information.
Maxwell described CoinJoin [12], a protocol for trust-
• Bitcoin: A (Very) Brief Introduction
• Cryptocurrency Analytics Methods
• GraphSense Cryptocurrency Analytics Platform
• Example Study: Ransomware Payments in the Bitcoin Ecosystem
• Future Research Directions
• Q & A
MY PLAN FOR TODAY
31
• Basic Functionality: 1Archive1n2C579dMsAu3iC6tWzuQJz8dN
• Micro-level analysis: 3Nxwenay9Z8Lc9JBiywExpnEFiLp6Afp8v (Bitcoin Rich List)
• Macro-level analysis: 1BjTR3NhTiVPKfbsZhrfx4vYKH4DLeh9UT (AlphaBay Market)
DEMOS
32
GRAPH CONSTRUCTION APPROACH
33
A
A A
AA
C
T
BlockchainAddress
Graph
Address
Cluster
Tags
Enrichmentprocess
[Haslhofer et al., 2016]
Statistics (as of Sept. 2017)
Transactions: 249,408,683
Addresses: 296,862,290
Clusters: 30,645,426
Address graph
- nodes (= addresses): 296,862,290
- edges (= aggregated transactions): 1,567,227,841
All data points are pre-computed and stored in
a de-normalized form
SOFTWARE COMPONENTS
34
OPEN SOURCE !
35
• Bitcoin: A (Very) Brief Introduction
• Cryptocurrency Analytics Methods
• GraphSense Cryptocurrency Analytics Platform
• Example Study: Ransomware Payments in the Bitcoin Ecosystem
• Future Research Directions
• Q & A
MY PLAN FOR TODAY
36
• Ransomware has become dominant
cybercrime threat
• Over 500 families
• Ransom payments almost exclusively in
Bitcoin
• More comprehensive, evidence-based
picture still missing
RANSOMWARE STUDY | MOTIVATION
37
38
Preliminary results are omitted in public slide deck.
Final results and paper is expected to published in Q2/2018
• Bitcoin: A (Very) Brief Introduction
• Cryptocurrency Analytics Methods
• GraphSense Cryptocurrency Analytics Platform
• Example Study: Ransomware Payments in the Bitcoin Ecosystem
• Future Research Directions
• Q & A
MY PLAN FOR TODAY
39
• Bitcoin linkability
• All transactions can be linked to prior outputs
• Allows construction of transaction, address, and cluster
graphs
• Monero obscures transactions by including chaff transaction
inputs
• Zcash supports shielded transactions to obscure parties and
amounts
• Ethereum is a “turing-complete” blockchain (smart contracts)
POST-BITCOIN CURRENCY ANALYTICS
40
• Working hypothesis:
• Bitcoin is supposed to be a decentralized system
• However, it has very strong centralization
tendencies
• Best example: Mining business
• Research Goals
• Better understanding of mining business across
ledgers
• Investigate economic behavior to infer “real”
structure of mining business
INSIGHT INTO THE MINING ECONOMY
41
• Cybercriminals increasingly turn their attention to
cryptocurrency services
• Major target: cryptocurrency exchanges
• Attacks are conducted in the same way as
targeted attacks on banks with similar or
sometimes identical tools and tactics
• Comeback of fraudulent schemes (Ponzi scheme,
investment scams, greater fool theory, etc.)
• Idea: systematic investigation and monitoring for
informed policy making
FINANCIAL CRIME FORENSICS
42
• If actors in the cryptocurrency ecosystem exceed
certain monetary thresholds, they might pose an
economic risk
• Ideas
• Develop network-based methods to quantify risks
• Stress test entire ecosystem
• ....
IDEA: SYSTEMIC RISK INVESTIGATION
43
O BITCOIN WHERE ART THOU?
An Introduction to Cryptocurrency Analytics
Dr. Bernhard Haslhofer
bernhard.haslhofer@ait.ac.at
EVERYWHERE !
Cryptocurrency analytics contributes to a better understanding
Dr. Bernhard Haslhofer
bernhard.haslhofer@ait.ac.at
• [Nakamoto, 2008]: Bitcoin: A peer-to-peer electronic cash system
• [Reid and Harrigan 2012]: An Analysis of Anonymity in the Bitcoin System
• [Meiklejohn, 2013]: A fistful of bitcoins: characterizing payments among men with no names
• [Ortega, 2013]: The bitcoin transaction graph—anonymity
• [Biryukov et al., 2014]: Deanonymisation of clients in Bitcoin P2P network
• [Fleder et. al, 2015]: Bitcoin Transaction Graph Analysis
• [Haslhofer et. al, 2016]: O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs.
• [Möser and Böhme, 2016]: Join Me on a Market for Anonymity
• [Harrigan and Fretter, 2016]: The Unreasonable Effectiveness of Address Clustering
• [Filtz et al, 2017]: Evolution of the Bitcoin Address Graph
• [Miller et al, 2017]: An Empirical Analysis of Linkability in the Monero Blockchain
• [Kumer et al, 2017]: A Traceability Analysis of Monero's Blockchain
• [Quesnelle 2017]: On the Linkability of ZCash transactions
REFERENCES
46

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O Bitcoin Where Art Thou? An Introduction to Cryptocurrency Analytics

  • 1. O BITCOIN WHERE ART THOU? An Introduction to Cryptocurrency Analytics Dr. Bernhard Haslhofer Oesterreichische Nationalbank (OeNB) | Research Seminar 2018-01-12
  • 2. 1. Which cryptocurrency should I buy? 2. What do you think about cryptocurrency X? 3. Where can I buy cryptocurrency X? Is it safe? 4. Can you help me setting up a cold wallet? 5. How will the price of cryptocurrency X evolve? MOST FREQUENT QUESTIONS I GET 2
  • 4. • Cryptocurrencies have entered mainstream • Public opinion is often misinformed and based on insufficient and / or unexamined evidence • Systematic scientific examination of entire cryptocurrency ecosystems still in its infancy • Missing methods, insufficient tool support MOTIVATION Image source: https://www.flickr.com/photos/namecoin/22995486509 4
  • 5. • Contribute to a better understanding of the structure and dynamics of cryptocurrency ecosystems • Multidisciplinary cooperation to answer specific (research) questions related to cryptocurrencies • Develop scalable quantitative methods, tools and services that help in answering those questions • Micro-level analysis: inspect atomic entities (block, transaction, address, currency flow) • Macro-level analysis: investigate real-world actors and services and their relationships OUR GOALS CRYPTOCURRENCY ANALYTICS 5
  • 6. INSIGHT INTO CRYPTOCURRENCY ECOSYSTEMS Global De-centralized Transparent Pseudo-Anonymous Complex, dynamic Networks Exchanges ATMs / Vouchers Payment Services Darknet Markets Mixing Services graphsense.info 30M clusters 480K blocks 1.5B relations 296M addresses 249M transactions For Whom? Science Public Authorities FinTech / Banks
  • 7. • Bitcoin: A (Very) Brief Introduction • Cryptocurrency Analytics Methods • GraphSense Cryptocurrency Analytics Platform • Example Study: Ransomware Payments in the Bitcoin Ecosystem • Future Research Directions • Q & A MY PLAN FOR TODAY 7
  • 11. TRANSACTION PROCESSING Find & Broadcast Block Bitcoin P2P Network Bitcoin Miners Blockchain 11
  • 13. ANATOMY OF A BITCOIN TRANSACTION 13 txid: a6b06e... blockhash: 0000ba7.. txid: 7f252a …. vout: 1 scriptSig: Signature value: 0.00460479 n: 0 addresses: [1Archive…] value: 0.00566296 n: 1 addresses: [1MuSWq…] List of inputs List of outputs Bitcoin Addresses Reference to unspent output of previous transaction (UTXO)
  • 14. ANATOMY OF A BITCOIN TRANSACTION 14 txid: a6b06e... blockhash: 0000ba7.. txid: 7f252a …. vout: 1 scriptSig: Signature value: 0.00460479 n: 0 addresses: [1Archive…] value: 0.00566296 n: 1 addresses: [1MuSWq…] sum(List of inputs) sum(List of outputs)≥
  • 15. ANATOMY OF A BITCOIN TRANSACTION 15 txid: a6b06e... blockhash: 0000ba7.. txid: 7f252a …. vout: 1 scriptSig: Signature value: 0.00460479 n: 0 addresses: [1Archive…] value: 0.00566296 n: 1 addresses: [1MuSWq…] sum(List of inputs) sum(List of outputs)−Transaction Fee =
  • 16. COINBASE TRANSACTION 16 txid: a60f6e2b... blockhash: 0000ba7.. coinbase: “0367c4...” value: 25.42394247 n: 0 addresses: [1KFHE7…]
  • 17. • Bitcoin: A (Very) Brief Introduction • Cryptocurrency Analytics Methods • GraphSense Cryptocurrency Analytics Platform • Example Study: Ransomware Payments in the Bitcoin Ecosystem • Future Research Directions • Q & A MY PLAN FOR TODAY 17
  • 18. My focus for today 18 TAXONOMY OF ANALYTICS METHODS Cryptocurrency Analytics P2P Network Analytics Blockchain Analytics Network Analytics Clustering Heuristics [Biryukov et al., 2014]
  • 19. 19 BLOCKCHAIN ANALYTICS Blockchain Analytics Network Analytics Clustering Heuristics Transaction Network Address Network
  • 20. 20 TRANSACTION NETWORK t1 t3 t2 t4 [Reid and Harrigan 2012] 0,00321 BTC 2016-03-14 17:33:50 directed acyclic temporal
  • 21. 21 TRANSACTION NETWORK | CONSTRUCTION txid: a6b06e... blockhash: 0000ba7.. txid: 7f252a …. vout: 1 scriptSig: Signature vin value: 0.00460479 n: 0 addresses: [1Archive…] vout Target node id Source node id Timestamp (via referenced block) Value
  • 22. 22 ADDRESS NETWORK a1 a2 a3 a5 a7 a8 a6 a9 a10 Estimated Value No. Transactions [Fleder et al. 2015, Filtz et al. 2017] (bi-)directed cyclic
  • 23. 23 ADDRESS NETWORK | CONSTRUCTION txid: a6b06e... blockhash: 0000ba7.. txid: 7f252a …. vout: 1 scriptSig: Signature vin value: 0.00460479 n: 0 addresses: [1Archive…] vout Source node id Target node id No Transactions = aggregated count of edges with same node ids
  • 24. 24 ESTIMATED VALUE | COMPUTATION inputs and outputs as shown in Table I. Fig. 2. Bitcoin transaction value assignment Therefore, we estimate the flow of actual Bitcoins betw two addresses using the following formula: TABLE I. BITCOINFLOW Transaction Formula Estimated BTC A1 A3 3 * (2/7) 0.857 A2 A3 3 * (5/7) 2.143 A1 A3 4 * (2/7) 1.143 A2 A4 4 * (5/7) 2.857 IV. ANALYSIS a1 → a3 = 3 ∗ & ' a1 tx a2 a3 a4 2 3 45 [Filtz et al. 2017]
  • 25. 25 BLOCKCHAIN ANALYTICS Blockchain Analytics Network Analysis Clustering Heuristics Multiple-Input Heuristics [Nakamoto, 2008] Change Heuristics [Meiklejohn, 2013] Temporal Behaviour [Ortega, 2013] Transaction Fingerprinting [Fleder et. al, 2015]
  • 29. c6c5 c4 c7 c3 c2 c1 29 CLUSTER / ENTITY NETWORK a1 a2 a3 a5 a7 a8 a6 a9 a10 Estimated Value No. Transactions directed cyclic
  • 30. • Address clustering • is a cornerstone of cryptocurrency analysis • partitions the set of addresses observed in the Bitcoin blockchain into maximal subsets of addresses that are likely controlled by the same real-world actor • Multiple input heuristics can identify more than 69% of the addresses stored by lightweight clients MULTIPLE INPUT HEURISTICS | EFFECTIVENESS 30 [Harrigan and Fretter, 2016] Figure 1. A graphical summary of the most significant flows of bitcoin between the largest address clusters during Bitcoin’s first five years in existence. The vertices correspond to address clusters: red vertices are darknet markets; purple vertices are gambling services; green vertices are exchanges and blue vertices are mining pools. The gray vertices are not immediately identifiable using publicly available information. Maxwell described CoinJoin [12], a protocol for trust-
  • 31. • Bitcoin: A (Very) Brief Introduction • Cryptocurrency Analytics Methods • GraphSense Cryptocurrency Analytics Platform • Example Study: Ransomware Payments in the Bitcoin Ecosystem • Future Research Directions • Q & A MY PLAN FOR TODAY 31
  • 32. • Basic Functionality: 1Archive1n2C579dMsAu3iC6tWzuQJz8dN • Micro-level analysis: 3Nxwenay9Z8Lc9JBiywExpnEFiLp6Afp8v (Bitcoin Rich List) • Macro-level analysis: 1BjTR3NhTiVPKfbsZhrfx4vYKH4DLeh9UT (AlphaBay Market) DEMOS 32
  • 33. GRAPH CONSTRUCTION APPROACH 33 A A A AA C T BlockchainAddress Graph Address Cluster Tags Enrichmentprocess [Haslhofer et al., 2016] Statistics (as of Sept. 2017) Transactions: 249,408,683 Addresses: 296,862,290 Clusters: 30,645,426 Address graph - nodes (= addresses): 296,862,290 - edges (= aggregated transactions): 1,567,227,841 All data points are pre-computed and stored in a de-normalized form
  • 36. • Bitcoin: A (Very) Brief Introduction • Cryptocurrency Analytics Methods • GraphSense Cryptocurrency Analytics Platform • Example Study: Ransomware Payments in the Bitcoin Ecosystem • Future Research Directions • Q & A MY PLAN FOR TODAY 36
  • 37. • Ransomware has become dominant cybercrime threat • Over 500 families • Ransom payments almost exclusively in Bitcoin • More comprehensive, evidence-based picture still missing RANSOMWARE STUDY | MOTIVATION 37
  • 38. 38 Preliminary results are omitted in public slide deck. Final results and paper is expected to published in Q2/2018
  • 39. • Bitcoin: A (Very) Brief Introduction • Cryptocurrency Analytics Methods • GraphSense Cryptocurrency Analytics Platform • Example Study: Ransomware Payments in the Bitcoin Ecosystem • Future Research Directions • Q & A MY PLAN FOR TODAY 39
  • 40. • Bitcoin linkability • All transactions can be linked to prior outputs • Allows construction of transaction, address, and cluster graphs • Monero obscures transactions by including chaff transaction inputs • Zcash supports shielded transactions to obscure parties and amounts • Ethereum is a “turing-complete” blockchain (smart contracts) POST-BITCOIN CURRENCY ANALYTICS 40
  • 41. • Working hypothesis: • Bitcoin is supposed to be a decentralized system • However, it has very strong centralization tendencies • Best example: Mining business • Research Goals • Better understanding of mining business across ledgers • Investigate economic behavior to infer “real” structure of mining business INSIGHT INTO THE MINING ECONOMY 41
  • 42. • Cybercriminals increasingly turn their attention to cryptocurrency services • Major target: cryptocurrency exchanges • Attacks are conducted in the same way as targeted attacks on banks with similar or sometimes identical tools and tactics • Comeback of fraudulent schemes (Ponzi scheme, investment scams, greater fool theory, etc.) • Idea: systematic investigation and monitoring for informed policy making FINANCIAL CRIME FORENSICS 42
  • 43. • If actors in the cryptocurrency ecosystem exceed certain monetary thresholds, they might pose an economic risk • Ideas • Develop network-based methods to quantify risks • Stress test entire ecosystem • .... IDEA: SYSTEMIC RISK INVESTIGATION 43
  • 44. O BITCOIN WHERE ART THOU? An Introduction to Cryptocurrency Analytics Dr. Bernhard Haslhofer bernhard.haslhofer@ait.ac.at
  • 45. EVERYWHERE ! Cryptocurrency analytics contributes to a better understanding Dr. Bernhard Haslhofer bernhard.haslhofer@ait.ac.at
  • 46. • [Nakamoto, 2008]: Bitcoin: A peer-to-peer electronic cash system • [Reid and Harrigan 2012]: An Analysis of Anonymity in the Bitcoin System • [Meiklejohn, 2013]: A fistful of bitcoins: characterizing payments among men with no names • [Ortega, 2013]: The bitcoin transaction graph—anonymity • [Biryukov et al., 2014]: Deanonymisation of clients in Bitcoin P2P network • [Fleder et. al, 2015]: Bitcoin Transaction Graph Analysis • [Haslhofer et. al, 2016]: O Bitcoin Where Art Thou? Insight into Large-Scale Transaction Graphs. • [Möser and Böhme, 2016]: Join Me on a Market for Anonymity • [Harrigan and Fretter, 2016]: The Unreasonable Effectiveness of Address Clustering • [Filtz et al, 2017]: Evolution of the Bitcoin Address Graph • [Miller et al, 2017]: An Empirical Analysis of Linkability in the Monero Blockchain • [Kumer et al, 2017]: A Traceability Analysis of Monero's Blockchain • [Quesnelle 2017]: On the Linkability of ZCash transactions REFERENCES 46