It was only a few years ago when cryptocurrency and blockchain research was a tiny and mostly confused field. That’s no longer the case! Research is growing rapidly and finding mainstream scholarly acceptance. To highlight a data point, the vibrancy and high-quality of Calls for Papers—to say nothing of the massive number of publications—is encouraging.

The earliest research was published circa 2012 and started to take off with the first Bitcoin bubble of 2013. Since then, the research landscape has expanded rapidly, with every discipline and nearly every topic represented. For example, the Blockchain Research Network bibliography contains over 2000 items. At the same time, anecdotally, the quality of research has risen significantly too.

This short description is an attempt to detail the breadth and depth of cryptocurrency and blockchain research. It will be frequently updated as the field grows and the landscape changes. I invite contributions and feedback.

See also Research Centers, Labs, and Classes for further information about active research and educational offerings.

Literature Reviews (7)

Li, Marier-Bienvenue, Perron-Brault, Wang, and Paré, (2018) conducted a scoping review that found 320 articles, which were narrowed by research questions to a final 39 articles that dealt with business research. They analysed the papers by topic, origin and date, and method.

Hawlitschek, Notheisen, and Teubner (2018) conducted a literature review on blockchain (but not cryptocurrencies), trust, and the sharing economy and found 62 articles. These articles are analysed in terms of 4 layers (NB, this conceptual framework is sui generis and not compatible with OSI layers): agent, application, infrastructure, and environment (with a fifth, top layer that the authors argue is the root of trust: behaviour).

Holub and Johnson (2018) found 1,206 academic articles focusing on Bitcoin (and only Bitcoin), which they organized into seven categories (Technology, Economics, Regulation, Critical Thought, Finance, Tax, and Accounting). They also noted that half the papers they collected were only available in preprint/manuscript version, which they (perhaps optimistically) attributed to research outpacing publication venues.

Risius and Spohrer (2017) conducted a scoping review of non-technical, blockchain (only) publications with a focus on “application, design, use, or implications of blockchain technology for humans, organizations, or markets,” finding 69 publications matching their criteria.

Oshodin, Molla, and Ong (2016) conducted a literature review on cryptocurrencies, blockchains, and digital currencies limited to information systems journals and found 81 papers. They organized the papers into four categories (End User, Organizational, System, and Research Directions).

Yli-Huum, Deokyoon, Choi, Park, and Smolander (2016) conducted a literature review on blockchains (loosely interpreted) and found 41 papers. They organized the papers into nine categories (Throughput, Latency, Size and Bandwidth, Security, Wasted Resources, Usability, Versioning etc., Privacy, and Others).

Morisse (2015) conducted a literature review and found 54 papers. These papers were organized according to four criteria (Protocol Layer, Network Layer, Ecosystem Layer).

Commentary

The research field is slowly but increasingly becoming reflexive, but common metrics, methods, and shared terminology still inhibit comparative or meta-analyses. It is interesting to note the very different (and largely incommensurate) landscape each review reveals. For example, some of the difference between Holub and Johnson’s very large study and the rest may be attributed to the prior including non-published literature, but this seems to be only part of the story. Other factors are surely at play.

Works cited




Research methodologies (60)

Most publications use a humanistic method or no declared method. However, social scientific, technical, and empirical measurement research methods are becoming increasingly common (see also Risius and Spohrer, 2017, who organized 69 publications into conceptual, theory-driven, and unknown/unclear methods).

What follows is an evolving survey of publications that discuss empirical research methods:

Research Method Bibliographic Details Description
Interview Van Hout, M. C., & Bingham, T. (2013). ‘Silk Road’, the virtual drug marketplace: A single case study of user experiences. International Journal of Drug Policy, 24(5), 385–391. Measures use and beliefs; n=1; Silk road users
Interview Zarifis, A., Efthymiou, L., Cheng, X., & Demetriou, S. (2014). Consumer Trust in Digital Currency Enabled Transactions. In International Conference on Business Information Systems (pp. 241–254). Larnaca, Cyprus: Springer. Retrieved from http://link.springer.com/10.1007/978-3-319-11460-6_21 Measures perceptions of trust in ecommerce; n=41; students
Interview Ingram, C., Morisse, M., & Teigland, R. (2015). “A Bad Apple Went Away”: Exploring Resilience among Bitcoin Entrepreneurs. Presented at the Twenty-Third European Conference on Information Systems (ECIS), Münster, Germany. Retrieved from https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2622066 Measures industry beliefs; n=5; Cryptocurrency company employees
Interview Oh, J., & Shong, I. (2017). A case study on business model innovations using Blockchain: focusing on financial institutions. Asia Pacific Journal of Innovation and Entrepreneurship, 11(3), 335–344. https://doi.org/10.1108/APJIE-12-2017-038 Measures industry beliefs; n=4; bank employees
User experience testing Kazerani, A., Rosati, D., & Lesser, B. (2017). Determining the Usability of Bitcoin for Beginners Using Change Tip and Coinbase. In Proceedings of the 35th ACM International Conference on the Design of Communication (pp. 5:1–5:5). New York, NY, USA: ACM. https://doi.org/10.1145/3121113.3121125 Measures user experience; n=2; students
Discourse analysis Vasek, M., Thornton, M., & Moore, T. (2014). Empirical Analysis of Denial-of-Service Attacks in the Bitcoin Ecosystem. In International Conference on Financial Cryptography and Data Security (pp. 57–71). Barbados: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-44774-1_5 Measures discussions of DDOS attacks; identifies 142 attacks
Ethnography Fletcher, J. (2013). Currency in Transition: An Ethnographic Inquiry of Bitcoin Adherents (Masters). University of Central Florida, Orlando, FL. Retrieved from http://stars.library.ucf.edu/etd/2748/ Measures Bitcoin user beliefs; n=14; Bitcoin community members
Mixed-method Van Hout, M. C., & Bingham, T. (2013). ‘Surfing the Silk Road’: A study of users’ experiences. International Journal of Drug Policy, 24(6), 524–529. Measures motivations; discourse analysis and interviews; n=20 (interviews); Silk road users
Mixed-method Lustig, C., & Nardi, B. (2015). Algorithmic Authority: The Case of Bitcoin (pp. 743–752). IEEE. https://doi.org/10.1109/HICSS.2015.95 Measures Bitcoin users’ beliefs; survey and interviews; n=510 (survey) & n=22 (interview); Bitcoin forum users
Mixed-method DuPont, Q. (2018). Experiments in Algorithmic Governance: An ethnography of “The DAO,” a failed Decentralized Autonomous Organization. In M. Campbell-Verduyn (Ed.), Bitcoin and Beyond: The Challenges and Opportunities of Blockchains for Global Governance. New York: Routledge. Measures blockchain users’ beliefs; auto-ethnography & discourse analysis; n=1; blockchain users
Survey Bohr, J., & Bashir, M. (2014). Who Uses Bitcoin? An exploration of the Bitcoin community. In 2014 Twelfth Annual International Conference on Privacy, Security and Trust (PST) (pp. 94–101). https://doi.org/10.1109/PST.2014.6890928 Measures Bitcoin user beliefs and demographics; n=1193; Bitcoin users on social media
Survey Polasik, M., Piotrowska, A. I., Wisniewski, T. P., Kotkowski, R., & Lightfoot, G. (2015). Price fluctuations and the use of Bitcoin: An empirical inquiry. International Journal of Electronic Commerce, 20(1), 9–49. Measures industry beliefs; n=108; general public
Survey Schuh, S., & Shy, O. (2015, December 3). US consumers’ adoption and use of Bitcoin and other virtual currencies. Retrieved from http://www.banqueducanada.ca/wp-content/uploads/2015/12/us-consumers-adoption.pdf Measures general knowledge and use of cryptocurrencies; n=3047; Americans
Survey Catalini, C., & Tucker, C. (2016). Seeding the s-curve? the role of early adopters in diffusion (No. 22596). National Bureau of Economic Research. Measures Bitcoin use; n=3108; MIT students
Survey Henry, C. S., Huynh, K. P., & Nicholls, G. (2017). Bitcoin Awareness and Usage in Canada (Staff Working Papers No. 17–56). Bank of Canada. Retrieved from https://ideas.repec.org/p/bca/bocawp/17-56.html Measures general knowledge and use of Bitcoin; n=1997; Canadians
Survey Hileman, G., & Rauchs, M. (2017). Global Cryptocurrency Benchmarking Study. Cambridge, England: University of Cambridge. Measures industry beliefs; n=144; cryptocurrency company employees
Survey Mourouzis, T., & Filipou, C. (2017). The Blockchain Revolution: Insights from Top-Management. ArXiv. Retrieved from http://arxiv.org/abs/1712.04649 Measures industry beliefs; n=42; cryptocurrency & blockchain company employees
Survey Athey, S., Catalini, C., & Tucker, C. (2017). The Digital Privacy Paradox: Small Money, Small Costs, Small Talk (Working Paper No. 23488). Cambridge MA: National Bureau Of Economic Research. Measures Bitcoin use; n=3108; MIT students
Survey Zamani, E. D., & Babatsikos, I. (2017). Diffusion and Adoption of Bitcoins In Light of The Financial Crisis: The case of Greece. In Proceedings of the 11th Mediterranean Conference on Information Systems (MCIS 2017). Genoa. Retrieved from https://www.dora.dmu.ac.uk/xmlui/handle/2086/14418 Measures Bitcoin use; n=24; Greek Bitcoin users
Passive measurement Dupont, J., & Squicciarini, A. C. (2015). Toward De-Anonymizing Bitcoin by Mapping Users Location. In Proceedings of the 5th ACM Conference on Data and Application Security and Privacy (pp. 139–141). San Antonio, Texas: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2699128 Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurement Paquet-Clouston, M., Haslhofer, B., & Dupont, B. (2018). Ransomware Payments in the Bitcoin Ecosystem. ArXiv. Retrieved from https://arxiv.org/pdf/1804.04080 Measures ransomware payments in Bitcoin; graph analysis
Passive measurement Zhao, C., & Guan, Y. (2015). A Graph-Based Investigation of Bitcoin Transactions. In Advances in Digital Forensics XI (pp. 79–95). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-319-24123-4_5 Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurement Christin, N. (2013). Traveling the Silk Road: A Measurement Analysis of a Large Anonymous Online Marketplace. In Proceedings of the 22nd International Conference on World Wide Web (pp. 213–224). New York, NY, USA: ACM. https://doi.org/10.1145/2488388.2488408 Measures Silk Road website with scraper; statistical analysis and price correlation with Bitcoin
Passive measurement Neudecker, T., Andelfinger, P., & Hartenstein, H. (2015). A Simulation Model for Analysis of Attacks on the Bitcoin Peer-to-Peer Network (pp. 1327–1332). Presented at the Integrated Network Management (IM), 2015 IFIP/IEEE International Symposium on, Ottawa, ON: IEEE. Retrieved from http://ieeexplore.ieee.org/abstract/document/7140490/ Measures Bitcoin network with simulation and attack
Passive measurement Decker, C., & Wattenhofer, R. (2013). Information propagation in the Bitcoin network. In IEEE P2P 2013 Proceedings (pp. 1–10). Trento, Italy: IEEE Communications Society. https://doi.org/10.1109/P2P.2013.6688704 Measures Bitcoin network with statistical analysis
Passive measurement Matzutt, R., Hiller, J., Henze, M., Ziegeldorf, J. H., Müllmann, D., Hohlfeld, O., & Wehrle, K. (2018). A Quantitative Analysis of the Impact of Arbitrary Blockchain Content on Bitcoin. In Proceedings of the 22nd International Conference on Financial Cryptography and Data Security (FC). Christ Church, Barbados: Springer. Measures Bitcoin blockchain transactions; visual inspection of illegal content
Passive measurement Koshy, P., Koshy, D., & McDaniel, P. (2014). An Analysis of Anonymity in Bitcoin Using P2P Network Traffic. In International Conference on Financial Cryptography and Data Security (pp. 469–485). Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-45472-5_30 Measures Bitcoin network with exploit client
Passive measurement Yin, H. S., & Vatrapu, R. (2017). A First Estimation of the Proportion of Cybercriminal Entities in the Bitcoin Ecosystem using Supervised Machine Learning. In 5th IEEE International Conference on Big Data. 2017 (pp. 3690–3699). IEEE. Measures Bitcoin blockchain with machine learning analysis; 874 observations
Passive measurement Maesa, D. D. F., Marino, A., & Ricci, L. (2017). Data-driven analysis of Bitcoin properties: exploiting the users graph. International Journal of Data Science and Analytics, 1–18. https://doi.org/10.1007/s41060-017-0074-x Measures Bitcoin blockchain graph with clustering; 100m transaction observations
Passive measurement Loi, H. (2017). The Liquidity of Bitcoin. International Journal of Economics and Finance, 10(1), 13. https://doi.org/10.5539/ijef.v10n1p13 Measures cryptocurrency exchange transaction data; 5 exchanges
Passive measurement Decker, C., & Wattenhofer, R. (2014). Bitcoin Transaction Malleability and MtGox. In Computer Security – ESORICS 2014 (pp. 313–326). Springer, Cham. https://doi.org/10.1007/978-3-319-11212-1_18 Measures double-spending in Bitcoin;  custom client detects 35202 potential attacks
Passive measurement Cocco, L., Concas, G., & Marchesi, M. (2015). Using an artificial financial market for studying a cryptocurrency market. Journal of Economic Interaction and Coordination, 1–21. Measures Bitcoin prices with simulated model of network
Passive measurement Bartoletti, M., & Pompianu, L. (2017). An analysis of Bitcoin OP_RETURN metadata. In International Conference on Financial Cryptography and Data Security (pp. 218–230). Springer. Measures Bitcoin OP_RETURN usage; 1.8m transactions
Passive measurement Androulaki, E., Karame, G. O., Roeschlin, M., Scherer, T., & Capkun, S. (2013). Evaluating user privacy in bitcoin. In International Conference on Financial Cryptography and Data Security (pp. 34–51). Okinawa, Japan: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-642-39884-1_4 Measures Bitcoin blockchain with simulation and clustering for de-anonymization; ~40% accounts partially de-anonymized
Passive measurement Ferrin, D. (2015). A Preliminary Field Guide for Bitcoin Transaction Patterns. In Proc. Texas Bitcoin Conf. Austin, Texas. Retrieved from https://bitbucket.org/numisight/explorer/downloads/TBC%202015%20Transaction%20Patterns.pdf Measures Bitcoin transactions to identify characterstic transaction types
Passive measurement Ermilov, D., Panov, M., & Yanovich, Y. (2017). Automatic Bitcoin Address Clustering. Presented at the IEEE International Conference on Machine Learning and Applications, Cancun, Mexico. Retrieved from http://bitfury.com/content/5-white-papers-research/clustering_whitepaper.pdf Measures Bitcoin transactions and de-anonymizes by linking to public information
Passive measurement Yanovich, Y., Mischenko, P., & Ostrovskiy, A. (2016). Shared send untangling in bitcoin. Working Paper, Bitfury Group Limited. Measures Bitcoin anonymizing technique; detects ~ 2.5% transactions
Passive measurement Karame, G. O., Androulaki, E., Roeschlin, M., Gervais, A., & Čapkun, S. (2015). Misbehavior in bitcoin: A study of double-spending and accountability. ACM Transactions on Information and System Security (TISSEC), 18(1), 2. Measures Bitcoin network for double-spending opportunities
Passive measurement Christin, N. (2013). Traveling the Silk Road: A Measurement Analysis of a Large Anonymous Online Marketplace. In Proceedings of the 22nd International Conference on World Wide Web (pp. 213–224). New York, NY, USA: ACM. https://doi.org/10.1145/2488388.2488408 Measures sales volume on Silk Road by daily website crawls; US $1.2m sales/month observed
Passive measurement Kristoufek, L. (2015). What Are the Main Drivers of the Bitcoin Price? Evidence from Wavelet Coherence Analysis. PLOS ONE, 10(4), e0123923. https://doi.org/10.1371/journal.pone.0123923 Measures Bitcoin price changes across three exchanges
Passive measurement Feder, A., Gandal, N., Hamrick, J. T., & Moore, T. (2016). The Impact of DDoS and Other Security Shocks on Bitcoin Currency Exchanges: Evidence from Mt. Gox. Presented at the Workshop on the Economics of Information Security (WEIS), Berkeley, CA. Retrieved from http://tylermoore.ens.utulsa.edu/weis16gox.pdf Measures impact of observed DDOS attacks on Mt. GOX exchange using leaked transaction data
Passive measurement Ron, D., & Shamir, A. (2013). Quantitative Analysis of the Full Bitcoin Transaction Graph. In Financial Cryptography and Data Security (pp. 6–24). Kralendijk, Bonaire: Springer. https://doi.org/10.1007/978-3-642-39884-1_2 Measures Bitcoin blockchain using graph analysis and statistical measures; 3.7m unique public keys discovered
Passive measurement Reid, F., & Harrigan, M. (2013). An analysis of anonymity in the bitcoin system. In Security and privacy in social networks (pp. 197–223). Springer. Measures Bitcoin transactions and de-anonymizes by linking to public information; de-anonymized ~2-5 Bitcoin wallets
Passive measurement Ober, M., Katzenbeisser, S., & Hamacher, K. (2013). Structure and Anonymity of the Bitcoin Transaction Graph. Future Internet, 5(2), 237–250. https://doi.org/10.3390/fi5020237 Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurement Santamaria Ortega, M. (2013). The Bitcoin transaction graph anonymity (Master’s). Universitat Oberta de Catalunya, Barcelona. Measures Bitcoin transactions and de-anonymizes by linking to public information
Passive measurement Fleder, M., Kester, M. S., & Pillai, S. (2015). Bitcoin transaction graph analysis. ArXiv. Retrieved from https://arxiv.org/pdf/1502.01657.pdf Measures Bitcoin transactions and de-anonymizes by linking to public information; detected 2322 users
Passive measurement Spagnuolo, M., Maggi, F., & Zanero, S. (2014). Bitiodine: Extracting Intelligence from the Bitcoin Network. In International Conference on Financial Cryptography and Data Security (pp. 457–468). Barbados: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-45472-5_29 Measures Bitcoin transactions and de-anonymizes by linking to public information and clustering; detected high-profile users
Passive measurement Baumann, A., Fabian, B., & Lischke, M. (2014). Exploring the Bitcoin Network (pp. 369–374). Presented at the International Conference on Web Information Systems and Technologies, Barcelona. Measures Bitcoin transactions and de-anonymizes by linking to public information and clustering
Passive measurement Feld, S., Schönfeld, M., & Werner, M. (2014). Analyzing the Deployment of Bitcoin’s P2P Network under an AS-level Perspective. Procedia Computer Science, 32, 1121–1126. Measures Bitcoin transactions
Passive measurement Fanti, G., & Viswanath, P. (2017). Deanonymization in the Bitcoin P2P Network. In Advances in Neural Information Processing Systems (pp. 1364–1373). Measures Bitcoin transactions and de-anonymizes by simulation
Passive measurement Lischke, M., & Fabian, B. (2016). Analyzing the bitcoin network: The first four years. Future Internet, 8(1), 7. Measures Bitcoin transactions and de-anonymizes by linking to public information; identified 223k distinct IP addresses, identified major accounts
Active measurement Meiklejohn, S., & Orlandi, C. (2015). Privacy-Enhancing Overlays in Bitcoin (pp. 127–141). Presented at the International Conference on Financial Cryptography and Data Security, Puerto Rico: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-48051-9_10 Measures Bitcoin transactions and made trades; 54 transactions
Active measurement Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., & Savage, S. (2013). A Fistful of Bitcoins: Characterizing Payments Among Men with No Names. In Proceedings of the 2013 Conference on Internet Measurement Conference (pp. 127–140). New York, NY, USA: ACM. https://doi.org/10.1145/2504730.2504747 Measures Bitcoin transactions by clustering for de-anonymizing; conducted range of activities (e.g., buying goods, gambling); 344 transactions
Active measurement Krafft, P. M., Della Penna, N., & Pentland, A. (2018). An Experimental Study of Cryptocurrency Market Dynamics. ArXiv. https://doi.org/10.1145/3173574.3174179 Buys cryptocurrency algorithmically to measure influence; induces ~2% increase in trade volume
Active measurement Biryukov, A., Khovratovich, D., & Pustogarov, I. (2014). Deanonymisation of clients in Bitcoin P2P network. CCS ’14 Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, 15–29. https://doi.org/10.1145/2660267.2660379 Attacks Bitcoin test network to de-anonymize users and performs denial of service to TOR network (11%-60% effective)
Active measurement Andrychowicz, M., Dziembowski, S., Malinowski, D., & Mazurek, L. (2015). On the malleability of bitcoin transactions (pp. 1–18). Presented at the International Conference on Financial Cryptography and Data Security, Puerto Rico: Springer. Retrieved from http://link.springer.com/chapter/10.1007/978-3-662-48051-9_1 Attacks Bitcoin transactions and clients  (41%-94% effective)
Active measurement Biryukov, A., & Pustogarov, I. (2015). Bitcoin over Tor isn’t a Good Idea. In 2015 IEEE Symposium on Security and Privacy (pp. 122–134). San Jose, CA: IEEE Computer Society. https://doi.org/10.1109/SP.2015.15 Attacks Bitcoin network to de-anonymize transactions; identified 11% of IPs using Bitcoin test network; attacked 100 Bitcoin addresses
Active measurement Gervais, A., Ritzdorf, H., Karame, G. O., & Capkun, S. (2015). Tampering with the delivery of blocks and transactions in bitcoin. In Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (pp. 692–705). Denver, Colorado: ACM. Retrieved from http://dl.acm.org/citation.cfm?id=2813655 Attacks Bitcoin network with denial of service (denied 2364 blocks); performs double-spend attack on testbed
Security analysis Nick, J. D. (2015). Data-Driven De-Anonymization in Bitcoin (Master’s Thesis). ETH-Zürich. Exploits previously unknown Bitcoin wallet code error to measure privacy in Bitcoin network
Security analysis Sari, A., & Kilic, S. (2017). Exploiting Cryptocurrency Miners with OISNT Techniques. Transactions on Networks and Communications, 5(6), 62. https://doi.org/10.14738/tnc.56.4083 Exploits known vulnerabilities to attack Bitcoin mining networks

Other Bibliographies (4)

There are four other bibliographies of research in the field, each with a different approach to scholarly publications (and unique limitations). The bibliographic collection of the Blockchain Research Network is differentiated from these other collections by its focus on resources and tools of use to academics, broad topical scope, and crowdsourced model of collection. The Blockchain Research Network bibliography has been cross-referenced with these bibliographies but is still growing and evolving.

Brett Scott’s “Bitcoin Academic Research

Features and limitations:

  • The first large-scale bibliography of research on cryptocurrencies and blockchains
  • No search or user-accessible metadata
  • No longer maintained

Christian Decker’s “Comprehensive Academic Bitcoin Research Archive

Features and limitations

  • Actively maintained
  • Bibtex available
  • No search

Blockchain Library’s “Academic Publications

Features and limitations:

  • Multiple categories
  • Limited to articles with DOIs
  • Not comprehensive
  • No search or user-accessible metadata

Marten Risius’s “Research State of the Art: Collaborative Database

  • Connected to research publication, with subject categories
  • Not comprehensive (95 publications)
  • No search or user-accessible metadata

Written by Quinn DuPont. Last updated September 2018.