The earliest research was published in 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 2400 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 MethodBibliographic DetailsDescription
InterviewVan 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
InterviewZarifis, 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_21Measures perceptions of trust in ecommerce; n=41; students
InterviewIngram, 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=2622066Measures industry beliefs; n=5; Cryptocurrency company employees
InterviewOh, 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-12-2017-038Measures industry beliefs; n=4; bank employees
User experience testingKazerani, 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/10.1145/3121113.3121125Measures user experience; n=2; students
Discourse analysisVasek, 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_5Measures discussions of DDOS attacks; identifies 142 attacks
EthnographyFletcher, 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-methodVan 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-methodLustig, C., & Nardi, B. (2015). Algorithmic Authority: The Case of Bitcoin (pp. 743–752). IEEE. https://doi.org/10.1109/HICSS.2015.95Measures Bitcoin users’ beliefs; survey and interviews; n=510 (survey) & n=22 (interview); Bitcoin forum users
Mixed-methodDuPont, 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
SurveyBohr, 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.2014.6890928Measures Bitcoin user beliefs and demographics; n=1193; Bitcoin users on social media
SurveyPolasik, 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
SurveySchuh, 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.pdfMeasures general knowledge and use of cryptocurrencies; n=3047; Americans
SurveyCatalini, 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
SurveyHenry, 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.htmlMeasures general knowledge and use of Bitcoin; n=1997; Canadians
SurveyHileman, G., & Rauchs, M. (2017). Global Cryptocurrency Benchmarking Study. Cambridge, England: University of Cambridge.Measures industry beliefs; n=144; cryptocurrency company employees
SurveyMourouzis, T., & Filipou, C. (2017). The Blockchain Revolution: Insights from Top-Management. ArXiv. Retrieved from http://arxiv.org/abs/1712.04649Measures industry beliefs; n=42; cryptocurrency & blockchain company employees
SurveyAthey, 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
SurveyZamani, 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/2086/14418Measures Bitcoin use; n=24; Greek Bitcoin users
Passive measurementDupont, 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=2699128Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurementPaquet-Clouston, M., Haslhofer, B., & Dupont, B. (2018). Ransomware Payments in the Bitcoin Ecosystem. ArXiv. Retrieved from https://arxiv.org/pdf/1804.04080Measures ransomware payments in Bitcoin; graph analysis
Passive measurementZhao, 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_5Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurementChristin, 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/10.1145/2488388.2488408Measures Silk Road website with scraper; statistical analysis and price correlation with Bitcoin
Passive measurementNeudecker, 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 measurementDecker, 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.2013.6688704Measures Bitcoin network with statistical analysis
Passive measurementMatzutt, 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 measurementKoshy, 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_30Measures Bitcoin network with exploit client
Passive measurementYin, 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 measurementMaesa, 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-xMeasures Bitcoin blockchain graph with clustering; 100m transaction observations
Passive measurementLoi, H. (2017). The Liquidity of Bitcoin. International Journal of Economics and Finance, 10(1), 13. https://doi.org/10.5539/ijef.v10n1p13Measures cryptocurrency exchange transaction data; 5 exchanges
Passive measurementDecker, 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_18Measures double-spending in Bitcoin;  custom client detects 35202 potential attacks
Passive measurementCocco, 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 measurementBartoletti, 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 measurementAndroulaki, 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_4Measures Bitcoin blockchain with simulation and clustering for de-anonymization; ~40% accounts partially de-anonymized
Passive measurementFerrin, 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.pdfMeasures Bitcoin transactions to identify characterstic transaction types
Passive measurementErmilov, 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.pdfMeasures Bitcoin transactions and de-anonymizes by linking to public information
Passive measurementYanovich, 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 measurementKarame, 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 measurementChristin, 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/10.1145/2488388.2488408Measures sales volume on Silk Road by daily website crawls; US $1.2m sales/month observed
Passive measurementKristoufek, 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.0123923Measures Bitcoin price changes across three exchanges
Passive measurementFeder, 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.pdfMeasures impact of observed DDOS attacks on Mt. GOX exchange using leaked transaction data
Passive measurementRon, 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_2Measures Bitcoin blockchain using graph analysis and statistical measures; 3.7m unique public keys discovered
Passive measurementReid, 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 measurementOber, 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/fi5020237Measures Bitcoin blockchain and transactions with statistical analysis
Passive measurementSantamaria 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 measurementFleder, M., Kester, M. S., & Pillai, S. (2015). Bitcoin transaction graph analysis. ArXiv. Retrieved from https://arxiv.org/pdf/1502.01657.pdfMeasures Bitcoin transactions and de-anonymizes by linking to public information; detected 2322 users
Passive measurementSpagnuolo, 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_29Measures Bitcoin transactions and de-anonymizes by linking to public information and clustering; detected high-profile users
Passive measurementBaumann, 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 measurementFeld, 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 measurementFanti, 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 measurementLischke, 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 measurementMeiklejohn, 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_10Measures Bitcoin transactions and made trades; 54 transactions
Active measurementMeiklejohn, 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/10.1145/2504730.2504747Measures Bitcoin transactions by clustering for de-anonymizing; conducted range of activities (e.g., buying goods, gambling); 344 transactions
Active measurementKrafft, P. M., Della Penna, N., & Pentland, A. (2018). An Experimental Study of Cryptocurrency Market Dynamics. ArXiv. https://doi.org/10.1145/3173574.3174179Buys cryptocurrency algorithmically to measure influence; induces ~2% increase in trade volume
Active measurementBiryukov, 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/10.1145/2660267.2660379Attacks Bitcoin test network to de-anonymize users and performs denial of service to TOR network (11%-60% effective)
Active measurementAndrychowicz, 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_1Attacks Bitcoin transactions and clients  (41%-94% effective)
Active measurementBiryukov, 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.2015.15Attacks Bitcoin network to de-anonymize transactions; identified 11% of IPs using Bitcoin test network; attacked 100 Bitcoin addresses
Active measurementGervais, 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=2813655Attacks Bitcoin network with denial of service (denied 2364 blocks); performs double-spend attack on testbed
Security analysisNick, 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 analysisSari, A., & Kilic, S. (2017). Exploiting Cryptocurrency Miners with OISNT Techniques. Transactions on Networks and Communications, 5(6), 62. https://doi.org/10.14738/tnc.56.4083Exploits known vulnerabilities to attack Bitcoin mining networks

Other Bibliographies (5)

There are five 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 (is a superset), and is actively maintained.

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 fulltext documents
  • No search

Blockchain Library

Features and limitations:

  • Multiple, thematic categories
  • Limited to articles with DOIs
  • No fulltext documents
  • No search or user-accessible metadata

Crypto Research Center

Features and limitations:

  • Simple metadata
  • Approximately 600 entries (January 2019)
  • No fulltext documents
  • Metadata search

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

  • Connected to research publication, with subject categories
  • 95 entries
  • No fulltext documents
  • No search or user-accessible metadata
  • No longer maintained

Written by Quinn DuPont. Last updated January 2019.