2017 Global Blockchain Benchmarking Study

2. Table of Contents 2 1. Blockchain and DLT 101 2. DLT Landscape 3. Use Cases and Business Models 4. Architecture and Governance 2017 Global Blockchain Benchmarking Study 5. Challenges and Interoperability 6. Public Sector 7. Appendices

3. Over 200 enterprise DLT start-ups, established corporations, central banks and other public sector institutions are included in the study sample* 32017 Global Blockchain Benchmarking Study *A number of survey respondents prefer not to have their participation disclosed. The names of participating central banks and other public sector institutions have been kept confidential. The survey data has been supplemented with secondary data sources.

4. Study Authors 4 Dr Garrick Hileman Research Fellow, Head of Cryptocurrency and Blockchain Research g.hileman@jbs.cam.ac.uk Michel Rauchs Research Assistant m.rauchs@jbs.cam.ac.uk 2017 Global Blockchain Benchmarking Study

5. Blockchain and DLT 101

6. What are blockchains and distributed ledgers? 62017 Global Blockchain Benchmarking Study

7. The five key components of a blockchain 72017 Global Blockchain Benchmarking Study

8. Why use a blockchain? 82017 Global Blockchain Benchmarking Study

9. Using a blockchain may help… 92017 Global Blockchain Benchmarking Study

10. Debunking common blockchain myths (1) 102017 Global Blockchain Benchmarking Study

14. Enterprise blockchain requirements vs. public blockchains (e.g., Bitcoin, Ethereum) 142017 Global Blockchain Benchmarking Study

15. Main types of blockchains segmented by permission model 152017 Global Blockchain Benchmarking Study Note: this study will focus exclusively on closed blockchains and distributed ledgers, with the exception of permissioned applications built on top of open blockchains.

16. The many different terms used for ‘blockchain’ sow confusion 162017 Global Blockchain Benchmarking Study

17. Distributed ledger technology (DLT) has gained popularity in 2016 as an umbrella term, but this trend appears to be receding 172017 Global Blockchain Benchmarking Study

18. Blockchains and distributed ledgers share properties with replicated and distributed databases 182017 Global Blockchain Benchmarking Study

19. Blockchain ⊂ Distributed Ledger ⊂ Distributed Database 192017 Global Blockchain Benchmarking Study

20. DLT Landscape

21. DLT system layers and an example of a DLT integrated ‘stack’ 212017 Global Blockchain Benchmarking Study

22. Main enterprise DLT ecosystem actors 222017 Global Blockchain Benchmarking Study

23. Overview of DLT software services providers 232017 Global Blockchain Benchmarking Study

24. Depiction of users and operators of a distributed ledger network 242017 Global Blockchain Benchmarking Study

25. Peripheral DLT ecosystem actors 252017 Global Blockchain Benchmarking Study

26. The number of specialised DLT start-ups has significantly increased since 2014: majority are active in developing infrastructure 262017 Global Blockchain Benchmarking Study

27. Several cryptocurrency-focused companies pivoted to DLT, primarily in 2014 and 2015 272017 Global Blockchain Benchmarking Study

28. Nearly half of all DLT start-ups are based in North America 282017 Global Blockchain Benchmarking Study

29. Estimated number of people working full-time on enterprise DLT 292017 Global Blockchain Benchmarking Study We estimate that established corporations have an additional several thousands of employees working full-time on DLT activities

30. Infrastructure providers have twice the median number of full- time employees as app developers and operators 302017 Global Blockchain Benchmarking Study

31. Use Cases and Business Models

32. The banking and finance industry has the largest number of identified DLT use cases 322017 Global Blockchain Benchmarking Study

33. Financial services and banking are the most frequently targeted sectors for DLT; increasing attention is given to non-monetary use cases 332017 Global Blockchain Benchmarking Study

34. Percentage of DLT platforms tracking different items 342017 Global Blockchain Benchmarking Study

35. Financial sector institutions are currently the main customers of DLT service providers 352017 Global Blockchain Benchmarking Study

36. Some impressions from survey data about actual DLT usage 362017 Global Blockchain Benchmarking Study

37. Three-quarters of study participants consider themselves to be software vendors 372017 Global Blockchain Benchmarking Study

38. It is more common for infrastructure providers to open-source their DLT codebase 382017 Global Blockchain Benchmarking Study

39. Open-source DLT codebases are most frequently licensed under Apache 2 and MIT 392017 Global Blockchain Benchmarking Study

40. Product acceptance is the primary reason given for open- sourcing enterprise DLT codebase 402017 Global Blockchain Benchmarking Study ’Other’ includes, among others, showcasing the quality of the codebase, fitting the overall marketing strategy, as well as facilitating interoperability and standardisation.

41. Business and revenue models used by enterprise DLT companies 412017 Global Blockchain Benchmarking Study

42. Most infrastructure providers use a combination of multiple revenue models, whereas operators most commonly seem to focus on a single model 422017 Global Blockchain Benchmarking Study There is still a significant degree of uncertainty among enterprise DLT ecosystem actors regarding revenue models

43. Infrastructure providers with open-source codebases tend to focus on providing consulting services whereas closed-source providers are often still undecided 432017 Global Blockchain Benchmarking Study

44. Monetisation primarily occurs at higher DLT stack levels: roles and lines between actors become increasingly blurred 442017 Global Blockchain Benchmarking Study

45. 75% of study participants have either fully operational production systems or are running advanced pilots 452017 Global Blockchain Benchmarking Study

46. DLT companies that provide software development services are at a more advanced stage of deployment than operators 462017 Global Blockchain Benchmarking Study

47. Lack of large-scale DLT deployments to date for a number of reasons 472017 Global Blockchain Benchmarking Study

48. Predictions about future trajectory of enterprise DLT ecosystem 482017 Global Blockchain Benchmarking Study • Core protocol layer will consolidate around a limited number of enterprise DLT frameworks and platforms, that each serve different business requirements and use cases • Significant number of small- to large-scale networks will be deployed (industry-specific, use case-specific, region-specific)

49. Architecture and Governance

50. Core DLT architectural building blocks 502017 Global Blockchain Benchmarking Study

51. While global data broadcast is still dominant, multi-channel data diffusion is rising 512017 Global Blockchain Benchmarking Study Multi-channel: data is only broadcast to selected parties involved in a specific transaction (‘selective disclosure’) Global: data is broadcast to all network participants

52. Overview of different approaches to storing data on-chain 522017 Global Blockchain Benchmarking Study One approach is not necessarily ’better’ than another as each has its advantages and drawbacks. It all depends on the acceptable trade-offs for the specific business case. This point applies in general to other DLT architectural design choices.

53. Operators predominantly store hashes (i.e., cryptographic fingerprints/digests of actual data) on-chain rather than the actual data 532017 Global Blockchain Benchmarking Study 51% of study participants support the integration of decentralised storage protocols and systems (e.g., IPFS, Siacoin, STORJ)

54. Reaching agreement on the global state of the ledger is the most common approach to consensus 542017 Global Blockchain Benchmarking Study Bilateral/multilateral: consensus is reached at the local level, i.e., only between participants involved in a specific transaction or trade Global: consensus is reached at the global level of the ledger, i.e., by all participants on the entire transaction history

55. Smart contracts: definition and differences in architecture 552017 Global Blockchain Benchmarking Study Smart contract: Simply put, a computer program that can automatically perform some function (e.g., make a payment). Smart contracts can live on a distributed ledger and can execute automatically once triggered by an event (e.g., payment is made once an asset is transferred).

56. The majority of industry actors integrate smart contracts with the legal system 562017 Global Blockchain Benchmarking Study In practice, many operators tie smart contract code to existing legal contracts, making them effectively legally enforceable ‘smart legal contracts’

57. Two-thirds of study participants use or support systems with extensive smart contract functionality 572017 Global Blockchain Benchmarking Study

58. Advantages and drawbacks of implementing business logic (smart contracts) at different layers 582017 Global Blockchain Benchmarking Study

59. Majority of operators have not implemented fully-functional smart contract capabilities, although most software vendors support them 592017 Global Blockchain Benchmarking Study It is not always clear whether the business logic resides at the core protocol layer or whether it is implemented on a separate, but linked layer on top

60. Smart contracts appear to be, for the most part, executed by every node in current implementations 602017 Global Blockchain Benchmarking Study

61. Gatekeepers/administrators of permissioned networks and applications can assume different roles 612017 Global Blockchain Benchmarking Study

62. Software services provide different models for selecting the gatekeeper of a permissioned system; currently 100% of operators act as gatekeepers in their network 622017 Global Blockchain Benchmarking Study

63. Software vendors predominantly maintain the codebase while operators approve software upgrades 632017 Global Blockchain Benchmarking Study

64. Offering regulators a node is the most common intended method for granting regulatory access to ledger data 642017 Global Blockchain Benchmarking Study ‘Other’ includes, among others, regulators receiving a full replica of sub- ledger transactions or being copied into each transaction they show a specific interest in

65. Tokenisation vs native assets (both tangible and intangible) 652017 Global Blockchain Benchmarking Study a) Issuance of new (native) assets: An asset (e.g., bond) is issued on a distributed ledger (‘primary issuance’): its existence is solely defined by the ledger, and so is ownership. The asset becomes a digital bearer asset in the sense that the entity controlling the corresponding private key owns the asset. b) Tokenisation of existing assets: An existing asset (e.g., gold held in custody) is digitally represented on a distributed ledger (‘tokenised’): the ledger keeps a record of ownership changes, but cannot enforce transfers of the underlying asset on-chain, as it is outside of its reach (‘off-chain’).

66. Support for tokenising existing assets and issuing new assets is significantly lower amongst operators 662017 Global Blockchain Benchmarking Study

67. Tokenising real-world assets will always require off-chain processes 672017 Global Blockchain Benchmarking Study

68. Challenges and Interoperability

69. Legal risks and an unclear regulatory environment are perceived as key inhibitors of broader DLT adoption, followed by privacy and a reluctance to change established practices 692017 Global Blockchain Benchmarking Study

70. Important takeaways from survey respondents on DLT challenges 702017 Global Blockchain Benchmarking Study

71. Wide range of additional challenges are slowing down broad enterprise DLT adoption 712017 Global Blockchain Benchmarking Study

72. Privacy is frequently cited as a key challenge; on-chain data encryption is the most common method for enhancing privacy 722017 Global Blockchain Benchmarking Study

73. Majority of DLT software roadmaps include the implementation of zero-knowledge proofs and ring signatures 732017 Global Blockchain Benchmarking Study

74. Performance and scalability claims from survey respondents 742017 Global Blockchain Benchmarking Study

75. Desired interoperability generally falls into two major categories 752017 Global Blockchain Benchmarking Study

76. Only 25% of DLT networks run by operators are interoperable with other DLT networks and applications 762017 Global Blockchain Benchmarking Study Lack of standards makes interoperability between networks built on different protocol specifications difficult to achieve

77. DLT interoperability is most common with Ethereum, Bitcoin and Hyperledger Fabric 772017 Global Blockchain Benchmarking Study

78. Percentage of DLT providers who are part of at least one industry initiative or consortium 782017 Global Blockchain Benchmarking Study

79. Public Sector

80. Countries where public sector institutions have publicly announced various DLT engagements; US has > 10 different institutions working on DLT, followed by UK and Russia with 4 802017 Global Blockchain Benchmarking Study

81. Public sector interest in DLT research programs and projects has become a global phenomenon 812017 Global Blockchain Benchmarking Study

82. European public sector institutions represent just under half the study sample, followed by Asia-Pacific (23%) 822017 Global Blockchain Benchmarking Study

83. Public sector study sample is approximately equally composed of central banks and other government institutions 832017 Global Blockchain Benchmarking Study

84. A quick note about the term ‘OPSIs’ – Other Public Sector Institutions 842017 Global Blockchain Benchmarking Study

85. We conservatively estimate that more than 500 public sector staff are working full-time on various DLT-related activities 852017 Global Blockchain Benchmarking Study

86. Central banks have in general more staff working on DLT- related activities than OPSIs 862017 Global Blockchain Benchmarking Study

87. Central banks are investigating a wide range of DLT uses beyond digital currency and payments 872017 Global Blockchain Benchmarking Study

88. Other use cases explored by central banks 882017 Global Blockchain Benchmarking Study

89. OPSIs are exploring a wide variety of DLT use cases, with managing identities and ownership records most common 892017 Global Blockchain Benchmarking Study 72% of OPSIs are exploring two or more different use cases, compared to 53% of central banks

90. Other use cases explored by OPSIs 902017 Global Blockchain Benchmarking Study

91. Benefits of using DLT – central banks 912017 Global Blockchain Benchmarking Study

92. Benefits of using DLT – OPSIs 922017 Global Blockchain Benchmarking Study

93. Majority of central banks and OPSIs are already engaged in proofs of concept and/or more advanced trials 932017 Global Blockchain Benchmarking Study

94. Central banks are engaged in more activities, but OPSI activities are more advanced in terms of deployment 942017 Global Blockchain Benchmarking Study

95. Two-thirds of central banks and 86% of OPSIs are directly experimenting with DLT protocols 952017 Global Blockchain Benchmarking Study

96. Ethereum is more frequently used by central banks than by other public sector institutions (OPSIs); 57% of central banks are experimenting with the Ethereum codebase* 962017 Global Blockchain Benchmarking Study *Note: some institutions are experimenting with multiple protocols. For example, several central banks are experimenting with both the public and permissioned versions of Ethereum, and so the total % of central banks testing some version of the Ethereum codebase is 57%

97. Differences exist between which protocols are actually being tested and what is publicly reported 972017 Global Blockchain Benchmarking Study

98. OPSIs more frequently undertake DLT projects in collaboration with DLT software vendors than do central banks 982017 Global Blockchain Benchmarking Study

99. DLT-related projects undertaken by central banks and OPSIs often involve the participation of a variety of different private sector actors 992017 Global Blockchain Benchmarking Study

100. Central banks are more actively collaborating on the international level than OPSIs; however, mostly information exchange 1002017 Global Blockchain Benchmarking Study

101. Majority of OPSIs plan to trial DLT this year; central banks are significantly more conservative 1012017 Global Blockchain Benchmarking Study

102. OPSIs are expressing a greater likeliness of DLT adoption in the next few years than central banks 1022017 Global Blockchain Benchmarking Study

103. Central banks are considerably more reserved about the impact of global DLT use in the public sector in the future 1032017 Global Blockchain Benchmarking Study

104. Key challenges to DLT adoption in the public sector 1042017 Global Blockchain Benchmarking Study

105. Challenges to DLT adoption in the public sector – central bank perspective 1052017 Global Blockchain Benchmarking Study

106. Challenges to DLT adoption in the public sector – OPSI perspective 1062017 Global Blockchain Benchmarking Study

107. Additional challenges mentioned by public sector institutions 1072017 Global Blockchain Benchmarking Study

108. Appendices

109. List of DLT use cases compiled from survey responses (1) 1092017 Global Blockchain Benchmarking Study

110. List of DLT use cases compiled from survey responses (2) 1102017 Global Blockchain Benchmarking Study

111. Nearly 90% of study participants indicate using a ‘blockchain’ data structure 1112017 Global Blockchain Benchmarking Study However, this does not imply that control over this data structure is necessarily decentralised – chaining hashes together has been common practice for decades (‘journaling’)

112. Glossary: Technology (1) 1122017 Global Blockchain Benchmarking Study

113. Glossary: Technology (2) 1132017 Global Blockchain Benchmarking Study

114. Glossary: DLT system 1142017 Global Blockchain Benchmarking Study

115. Glossary: Enterprise DLT ecosystem actors 1152017 Global Blockchain Benchmarking Study

116. A note on the term ‘validators’ 1162017 Global Blockchain Benchmarking Study Suggested alternative terms: • Blockchains: block signers • Non-blockchain distributed ledgers: consensus nodes

2017 Global Blockchain Benchmarking Study

2017 Global Blockchain Benchmarking Study

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