Big-Crypto: Big Data, Blockchain and Cryptocurrency
Abstract
:1. Introduction
2. Cryptocurrency
2.1. Blockchain
2.1.1. Blockchain and Tangle and Hashgraph
2.1.2. Blockchain and Artificial Intelligence (AI)
2.2. Trends
3. When Cryptocurrency Meets Big Data
3.1. Security and Privacy Enhancement
3.2. Analyses and Prediction
4. Conclusions and Future Research
Author Contributions
Funding
Conflicts of Interest
References
- Iinuma, A. Why Is the Cryptocurrency Market So Volatile: Expert Take. 2018. Available online: https://cointelegraph.com/news/why-is-the-cryptocurrency-market-so-volatile-expert-take (accessed on 30 August 2018).
- Browne, R.; Kharpal, A. Cryptocurrency Market Will Hit $1 Trillion Valuation This Year, CEO of Top Exchange Says. 2018. Available online: https://www.cnbc.com/2018/02/13/cryptocurrency-market-to-hit-1-trillion-valuation-in-2018-kraken-ceo.html (accessed on 30 August 2018).
- Hwang, K.; Chen, M. Big-Data Analytics for Cloud, IoT and Cognitive Computing; John Wiley & Sons: Hoboken, NJ, USA, 2017. [Google Scholar]
- Morgan, J. A Simple Explanation of ‘The Internet of Things’. 2014. Available online: https://www.forbes.com/sites/jacobmorgan/2014/05/13/simple-explanation-internet-things-that-anyone-can-understand/2a28a25b1d09 (accessed on 30 August 2018).
- Microsoft. What Is Cloud Computing? A Beginner’s Guide. 2018. Available online: https://azure.microsoft.com/en-us/overview/what-is-cloud-computing/ (accessed on 30 August 2018).
- Lu, H.; Li, Y.; Chen, M.; Kim, H.; Serikawa, S. Brain intelligence: Go beyond artificial intelligence. Mobile Netw. Appl. 2018, 23, 368–375. [Google Scholar] [CrossRef]
- Chen, M.; Hao, Y.; Hwang, K.; Wang, L.; Wang, L. Disease prediction by machine learning over big data from healthcare communities. IEEE Access 2017, 5, 8869–8879. [Google Scholar] [CrossRef]
- Chen, M.; Yang, J.; Hao, Y.; Mao, S.; Hwang, K. A 5G cognitive system for healthcare. Big Data Cognit. Comput. 2017, 1, 2. [Google Scholar] [CrossRef]
- Chen, M.; Li, W.; Hao, Y.; Qian, Y.; Humar, I. Edge cognitive computing based smart healthcare system. Future Gener. Comput. Syst. 2018, 86, 403–411. [Google Scholar] [CrossRef]
- Ramírez-Gallego, S.; Fernández, A.; García, S.; Chen, M.; Herrera, F. Big data: Tutorial and guidelines on information and process fusion for analytics algorithms with MapReduce. Inf. Fusion 2018, 42, 51–61. [Google Scholar] [CrossRef]
- Wamba, S.F.; Akter, S.; Edwards, A.; Chopin, G.; Gnanzou, D. How ‘big data’ can make big impact: Findings from a systematic review and a longitudinal case study. Int. J. Prod. Econ. 2015, 165, 234–246. [Google Scholar] [CrossRef]
- Nakamoto, S. Bitcoin: A Peer-to-Peer Electronic Cash System. 2008. Available online: https://bitcoin.org/bitcoin.pdf (accessed on 30 August 2018).
- Mourdoukoutas, P. Bitcoin, Ethereum and Litecoin Are the Most Popular Cryptocurrency Investments Among Millennials. 2018. Available online: https://www.forbes.com/sites/panosmourdoukoutas/2018/03/25/bitcoin-ethereum-and-litecoin-are-the-most-popular-cryptocurrency-investments-among-millennials/1a20855876dd (accessed on 30 August 2018).
- Hoffman, C. Why It’s Nearly Impossible to Make Money Mining Bitcoin. 2018. Available online: https://www.howtogeek.com/349033/why-it%E2%80%99s-nearly-impossible-to-make-money-mining-bitcoin/ (accessed on 30 August 2018).
- Narayanan, A.; Bonneau, J.; Felten, E.; Miller, A.; Goldfeder, S. Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction; Princeton University Press: Princeton, NJ, USA, 2016. [Google Scholar]
- Tschorsch, F.; Scheuermann, B. Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutor. 2016, 18, 2084–2123. [Google Scholar] [CrossRef]
- Yermack, D. Is Bitcoin a real currency? An economic appraisal. In Handbook of Digital Currency; Academic Press: Cambridge, MA, USA, 2015; pp. 31–43. [Google Scholar]
- Fry, J.; Cheah, E.T. Negative bubbles and shocks in cryptocurrency markets. Int. Rev. Financ. Anal. 2016, 47, 343–352. [Google Scholar] [CrossRef]
- Tiwari, A.K.; Jana, R.K.; Das, D.; Roubaud, D. Informational efficiency of Bitcoin—An extension. Econ. Lett. 2018, 163, 106–109. [Google Scholar] [CrossRef]
- Bariviera, A.F. The inefficiency of Bitcoin revisited: A dynamic approach. Econ. Lett. 2017, 161, 1–4. [Google Scholar] [CrossRef][Green Version]
- Nadarajah, S.; Chu, J. On the inefficiency of Bitcoin. Econ. Lett. 2017, 150, 6–9. [Google Scholar] [CrossRef]
- Urquhart, A. The inefficiency of Bitcoin. Econ. Lett. 2016, 148, 80–82. [Google Scholar] [CrossRef]
- Peng, Y.; Albuquerque, P.H.M.; de Sa, J.M.C.; Padula, A.J.A.; Montenegro, M.R. The best of two worlds: Forecasting high frequency volatility for cryptocurrencies and traditional currencies with Support Vector Regression. Expert Syst. Appl. 2018, 97, 177–192. [Google Scholar] [CrossRef]
- Jiang, Y.; Nie, H.; Ruan, W. Time-varying long-term memory in Bitcoin market. Financ. Res. Lett. 2018, 25, 280–284. [Google Scholar] [CrossRef]
- Khuntia, S.; Pattanayak, J.K. Adaptive market hypothesis and evolving predictability of bitcoin. Econ. Lett. 2018, 167, 26–28. [Google Scholar] [CrossRef]
- Alvarez-Ramirez, J.; Rodriguez, E.; Ibarra-Valdez, C. Long-range correlations and asymmetry in the Bitcoin market. Phys. A Stat. Mech. Appl. 2018, 492, 948–955. [Google Scholar] [CrossRef]
- Katsiampa, P. Volatility estimation for Bitcoin: A comparison of GARCH models. Econ. Lett. 2017, 158, 3–6. [Google Scholar] [CrossRef]
- Balcilar, M.; Bouri, E.; Gupta, R.; Roubaud, D. Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Econ. Model. 2017, 64, 74–81. [Google Scholar] [CrossRef]
- Dyhrberg, A.H. Bitcoin, gold and the dollar—A GARCH volatility analysis. Financ. Res. Lett. 2016, 16, 85–92. [Google Scholar] [CrossRef]
- Bouri, E.; Azzi, G.; Dyhrberg, A.H. On the Return-Volatility Relationship in the Bitcoin Market around the Price Crash of 2013. 2016. Available online: https://ssrn.com/abstract=2869855 (accessed on 30 August 2018).[Green Version]
- Demir, E.; Gozgor, G.; Lau, C.K.M.; Vigne, S.A. Does economic policy uncertainty predict the Bitcoin returns? An empirical investigation. Financ. Res. Lett. 2018, 26, 145–149. [Google Scholar] [CrossRef]
- Ciaian, P.; Rajcaniova, M.; Kancs, D.A. The economics of BitCoin price formation. Appl. Econ. 2016, 48, 1799–1815. [Google Scholar] [CrossRef]
- Bouri, E.; Gupta, R.; Tiwari, A.K.; Roubaud, D. Does Bitcoin hedge global uncertainty? Evidence from wavelet-based quantile-in-quantile regressions. Financ. Res. Lett. 2017, 23, 87–95. [Google Scholar] [CrossRef]
- Hayes, A.S. Cryptocurrency value formation: An empirical study leading to a cost of production model for valuing bitcoin. Telemat. Inf. 2017, 34, 1308–1321. [Google Scholar] [CrossRef]
- Yelowitz, A.; Wilson, M. Characteristics of Bitcoin users: An analysis of Google search data. Appl. Econ. Lett. 2015, 22, 1030–1036. [Google Scholar] [CrossRef]
- Pieters, G.; Vivanco, S. Financial regulations and price inconsistencies across Bitcoin markets. Inf. Econ. Policy 2017, 39, 1–14. [Google Scholar] [CrossRef]
- Bouri, E.; Gupta, R.; Lau, C.K.M.; Roubaud, D.; Wang, S. Bitcoin and global financial stress: A copula-based approach to dependence and causality in the quantiles. Q. Rev. Econ. Financ. 2018, 69, 297–307. [Google Scholar] [CrossRef]
- Naresh, P. Regulators Finally Conclude Blockchain and Bitcoin are “Inseparable”. 2018. Available online: https://zycrypto.com/regulators-finally-conclude-blockchain-and-bitcoin-are-inseparable/ (accessed on 30 August 2018).
- Iansiti, M.; Lakhani, K.R. The truth about blockchain. Harv. Bus. Rev. 2017, 95, 118–127. [Google Scholar]
- Crosby, M.; Pattanayak, P.; Verma, S.; Kalyanaraman, V. Blockchain technology: Beyond bitcoin. Appl. Innov. 2016, 2, 6–10. [Google Scholar]
- Kosba, A.; Miller, A.; Shi, E.; Wen, Z.; Papamanthou, C. Hawk: The blockchain model of cryptography and privacy-preserving smart contracts. In Proceedings of the 2016 IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA, 22–26 May 2016. [Google Scholar]
- Christidis, K.; Devetsikiotis, M. Blockchains and smart contracts for the internet of things. IEEE Access 2016, 4, 2292–2303. [Google Scholar] [CrossRef]
- Gatteschi, V.; Lamberti, F.; Demartini, C.; Pranteda, C.; Santamaría, V. Blockchain and Smart Contracts for Insurance: Is the Technology Mature Enough? Future Int. 2018, 10, 20. [Google Scholar] [CrossRef]
- Turkanović, M.; Hölbl, M.; Košič, K.; Heričko, M.; Kamišalić, A. EduCTX: A blockchain-based higher education credit platform. IEEE Access 2018, 6, 5112–5127. [Google Scholar] [CrossRef]
- Pilkington, M. 11 Blockchain technology: Principles and applications. In Research Handbook on Digital Transformations; Edward Elgar: Cheltenham, UK, 2016; p. 225. [Google Scholar]
- Yue, X.; Wang, H.; Jin, D.; Li, M.; Jiang, W. Healthcare data gateways: found healthcare intelligence on blockchain with novel privacy risk control. J. Med. Syst. 2016, 40, 218. [Google Scholar] [CrossRef] [PubMed]
- Tse, D.; Zhang, B.; Yang, Y.; Cheng, C.; Mu, H. Blockchain application in food supply information security. In Proceedings of the 2017 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM), Singapore, 10–13 December 2017. [Google Scholar]
- Chapron, G. The environment needs cryptogovernance. Nature 2017, 545, 403–405. [Google Scholar] [CrossRef] [PubMed]
- Basden, J.; Cottrell, M. How utilities are using blockchain to modernize the grid. Harv. Bus. Rev. 2017, 3, 1–3. [Google Scholar]
- Sikorski, J.J.; Haughton, J.; Kraft, M. Blockchain technology in the chemical industry: Machine-to-machine electricity market. Appl. Energy 2017, 195, 234–246. [Google Scholar] [CrossRef]
- Ma, Z.; Jiang, M.; Gao, H.; Wang, Z. Blockchain for digital rights management. Future Gener. Comput. Syst. 2018, 89, 746–764. [Google Scholar] [CrossRef]
- Wang, B.; Sun, J.; He, Y.; Pang, D.; Lu, N. Large-scale Election Based On Blockchain. Proced. Comput. Sci. 2018, 129, 234–237. [Google Scholar] [CrossRef]
- Reyna, A.; Martin, C.; Chen, J.; Soler, E.; Diaz, M. On blockchain and its integration with IoT. Challenges and opportunities. Future Gener. Comput. Syst. 2018, 88, 173–190. [Google Scholar] [CrossRef]
- Yli-Huumo, J.; Ko, D.; Choi, S.; Park, S.; Smolander, K. Where is current research on blockchain technology?—A systematic review. PLoS ONE 2016, 11, e0163477. [Google Scholar] [CrossRef] [PubMed]
- Berke, A. How Safe Are Blockchains? It Depends. Harvard Business Review. 2017. Available online: https://hbr.org/2017/03/how-safe-are-blockchains-it-depends (accessed on 30 August 2018).
- Vranken, H. Sustainability of bitcoin and blockchains. Curr. Opin. Environ. Sustain. 2017, 28, 1–9. [Google Scholar] [CrossRef]
- Omaar, J. Forever Isn’t Free: The Cost of Storage on a Blockchain Database. 2017. Available online: https://medium.com/ipdb-blog/forever-isnt-free-the-cost-of-storage-on-a-blockchain-database-59003f63e01 (accessed on 30 August 2018).
- Fairley, P. The Ridiculous Amount of Energy It Takes to Run Bitcoin. 2017. Available online: https://spectrum.ieee.org/energy/policy/the-ridiculous-amount-of-energy-it-takes-to-run-bitcoin (accessed on 30 August 2018).
- Tapscott, A.; Tapscott, D. How Blockchain Is Changing Finance. Harvard Business Review. 2017. Available online: https://hbr.org/2017/03/how-blockchain-is-changing-finance (accessed on 30 August 2018).
- Yeoh, P. Regulatory issues in blockchain technology. J. Financ. Regul. Compliance 2017, 25, 196–208. [Google Scholar] [CrossRef]
- Risberg, J. Yes, the Blockchain Can Be Hacked. 2018. Available online: https://coincentral.com/blockchain-hacks/ (accessed on 30 August 2018).
- Mendling, J.; Weber, I.; Aalst, W.V.D.; Brocke, J.V.; Cabanillas, C.; Daniel, F.; Debois, S.; Di Ciccio, C.; Dumas, M.; Gal, A.; et al. Blockchains for business process management-challenges and opportunities. ACM Trans. Manag. Inf. Syst. 2018, 9. [Google Scholar] [CrossRef]
- Kiktenko, E.O.; Pozhar, N.O.; Anufriev, M.N.; Trushechkin, A.S.; Yunusov, R.R.; Kurochkin, Y.V.; Fedorov, A.K. Quantum-secured blockchain. Quantum Sci. Technol. 2018, 3, 035004. [Google Scholar] [CrossRef][Green Version]
- Floyd, D. What Is the Tangle, and Is It Blockchain’s ‘Next Evolutionary Step’? 2018. Available online: https://www.nasdaq.com/article/what-is-the-tangle-and-is-it-blockchains-next-evolutionary-step-cm911074 (accessed on 30 August 2018).
- Stein, S. Hashgraph Wants to Give You the Benefits of Blockchain without the Limitations. 2018. Available online: https://techcrunch.com/2018/03/13/hashgraph-wants-to-give-you-the-benefits-of-blockchain-without-the-limitations/ (accessed on 30 August 2018).
- Schueffel, P. Alternative Distributed Ledger Technologies Blockchain vs. Tangle vs. Hashgraph-A High-Level Overview and Comparison. 2017. Available online: https://ssrn.com/abstract=3144241 (accessed on 30 August 2018).
- Popov, S. The Tangle. Version 1.4.3. 2018. Available online: https://www.iota.org/research/academic-papers (accessed on 30 August 2018).
- Cachin, C.; Vukolic, M. Blockchains consensus protocols in the wild. arXiv, 2017; arXiv:1707.01873. [Google Scholar]
- Baird, L. The Swirlds Hashgraph Consensus Algorithm: Fair, Fast, Byzantine Fault Tolerance; Swirlds, Inc.: College Station, TX, USA, 2016. [Google Scholar]
- McCarthy, J. What Is Artificial Intelligence. 2007. Available online: http://wwwformal.stanford.edu/jmc/whatisai/whatisai.html (accessed on 30 August 2018).
- Gokani, J. The Evolution of Banking: AI. 2014. Available online: https://mse238blog.stanford.edu/2017/08/jgokani/the-evolution-of-banking-ai/ (accessed on 30 August 2018).
- Fethi, M.D.; Pasiouras, F. Assessing bank efficiency and performance with operational research and artificial intelligence techniques: A survey. Eur. J. Oper. Res. 2010, 204, 189–198. [Google Scholar] [CrossRef]
- Huerta, J.M.; Anand, A. Machine learning and artificial intelligence in consumer banking. J. Digit. Bank. 2018, 3, 22–32. [Google Scholar]
- Marr, B. A Short History of Machine Learning—Every Manager Should Read. 2016. Available online: https://www.forbes.com/sites/bernardmarr/2016/02/19/a-short-history-of-machine-learning-every-manager-should-read/6724e74315e7 (accessed on 30 August 2018).
- Hassani, H.; Huang, X.; Silva, E.S. Digitalisation and Big Data Mining in Banking. Big Data Cognit. Comput. 2018, 2, 18. [Google Scholar] [CrossRef]
- Russell, S.J.; Norvig, P. Artificial Intelligence: A Modern Approach; Pearson Education Limited: Kuala Lumpur, Malaysia, 2016. [Google Scholar]
- Baird, C. Cryptocurrency and AI All the Buzz at Tech Conference Slush Tokyo. 2018. Available online: https://www.japantimes.co.jp/news/2018/03/29/business/cryptocurrency-ai-excite-tech-confab-slush-tokyo/.W4hZE-hKj6Q (accessed on 30 August 2018).
- Terman, M. Do A.I. and Cryptocurrency Work Well Together? 2017. Available online: https://bitcoinist.com/ai-and-cryptocurrency-work-well-together/ (accessed on 30 August 2018).
- Aitken, R. Can the ‘AI Blockchain’ Combo Finally Crack the Crypto Market? 2018. Available online: https://www.forbes.com/sites/rogeraitken/2018/05/31/can-the-ai-blockchain-combo-finally-crack-the-crypto-market/6dfe42015a13 (accessed on 30 August 2018).
- Corea, F. The Convergence of AI and Blockchain. In Applied Artificial Intelligence: Where AI Can Be Used In Business; Springer: Cham, Switzerland, 2018; pp. 19–26. [Google Scholar]
- Hassani, H.; Huang, X.; Silva, E.S.; Ghodsi, M. A review of data mining applications in crime. Stat. Anal. Data Min. ASA Data Sci. J. 2016, 9, 139–154. [Google Scholar] [CrossRef]
- Hassani, H.; Huang, X.; Ghodsi, M. Big Data and Causality. Ann. Data Sci. 2017, 5, 1–24. [Google Scholar] [CrossRef]
- Hassani, H.; Silva, E.S. Big Data: A big opportunity for the petroleum and petrochemical industry. OPEC Energy Rev. 2018, 42, 74–89. [Google Scholar] [CrossRef]
- Hassani, H.; Silva, E.S. Forecasting with big data: A review. Ann. Data Sci. 2015, 2, 5–19. [Google Scholar] [CrossRef][Green Version]
- Chuen, D.L.K. (Ed.) Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data; Academic Press: Cambridge, MA, USA, 2015. [Google Scholar]
- Karafiloski, E.; Mishev, A. Blockchain solutions for big data challenges: A literature review. In Proceedings of the IEEE EUROCON 2017—17th International Conference on Smart Technologies, Ohrid, Macedonia, 6–8 July 2017. [Google Scholar]
- Li, X.; Jiang, P.; Chen, T.; Luo, X.; Wen, Q. A survey on the security of blockchain systems. Future Gener. Comput. Syst. 2017. [Google Scholar] [CrossRef][Green Version]
- Azaria, A.; Ekblaw, A.; Vieira, T.; Lippman, A. Medrec: Using blockchain for medical data access and permission management. In Proceedings of the 2016 2nd International Conference on Open and Big Data (OBD), Vienna, Austria, 22–24 August 2016. [Google Scholar]
- Liang, X.; Zhao, J.; Shetty, S.; Liu, J.; Li, D. Integrating blockchain for data sharing and collaboration in mobile healthcare applications. In Proceedings of the 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Montreal, QC, Canada, 8–13 October 2017. [Google Scholar]
- Zhang, P.; White, J.; Schmidt, D.C.; Lenz, G. Applying software patterns to address interoperability in blockchain-based healthcare apps. arXiv, 2017; arXiv:1706.03700. [Google Scholar]
- Griggs, K.N.; Ossipova, O.; Kohlios, C.P.; Baccarini, A.N.; Howson, E.A.; Hayajneh, T. Healthcare Blockchain System Using Smart Contracts for Secure Automated Remote Patient Monitoring. J. Med. Syst. 2018, 42, 130. [Google Scholar] [CrossRef] [PubMed]
- Kuo, T.T.; Kim, H.E.; Ohno-Machado, L. Blockchain distributed ledger technologies for biomedical and health care applications. J. Am. Med. Inf. Assoc. 2017, 24, 1211–1220. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Mendez, D.M.; Papapanagiotou, I.; Yang, B. Internet of things: Survey on security and privacy. arXiv, 2017; arXiv:1707.01879. [Google Scholar]
- Khan, M.A.; Salah, K. IoT security: Review, blockchain solutions, and open challenges. Future Gener. Comput. Syst. 2018, 82, 395–411. [Google Scholar] [CrossRef]
- Lei, A.; Cruickshank, H.; Cao, Y.; Asuquo, P.; Ogah, C.P.A.; Sun, Z. Blockchain-based dynamic key management for heterogeneous intelligent transportation systems. IEEE Int. Things J. 2017, 4, 1832–1843. [Google Scholar] [CrossRef]
- Singh, A.; Kumar, D.; Hotzel, J. IoT based Information and Communication System for Enhancing Underground Mines Safety and Productivity: Genesis, Taxonomy and Open Issues. Ad Hoc Netw. 2018, 78, 115–129. [Google Scholar] [CrossRef]
- Biswas, K.; Muthukkumarasamy, V. Securing smart cities using blockchain technology. In Proceedings of the 2016 IEEE 18th International Conference on High Performance Computing and Communications; IEEE 14th International Conference on Smart City; IEEE 2nd International Conference on Data Science and Systems (HPCC/SmartCity/DSS), Sydney, NSW, Australia, 12–14 December 2016; pp. 1392–1393. [Google Scholar]
- Dorri, A.; Kanhere, S.S.; Jurdak, R.; Gauravaram, P. Blockchain for IoT security and privacy: The case study of a smart home. In Proceedings of the 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), Kona, HI, USA, 13–17 March 2017; pp. 618–623. [Google Scholar]
- Hammi, M.T.; Hammi, B.; Bellot, P.; Serhrouchni, A. Bubbles of Trust: a decentralized Blockchain-based authentication system for IoT. Comput. Secur. 2018, 78, 126–142. [Google Scholar] [CrossRef]
- Qu, C.; Tao, M.; Zhang, J.; Hong, X.; Yuan, R. Blockchain Based Credibility Verification Method for IoT Entities. Secur. Commun. Netw. 2018, 2018, 7817614. [Google Scholar] [CrossRef]
- Park, J.H.; Park, J.H. Blockchain security in cloud computing: Use cases, challenges, and solutions. Symmetry 2017, 9, 164. [Google Scholar] [CrossRef]
- Kshetri, N. Can blockchain strengthen the internet of things? IT Prof. 2017, 19, 68–72. [Google Scholar] [CrossRef]
- Yin, H.S.; Vatrapu, R. A first estimation of the proportion of cybercriminal entities in the bitcoin ecosystem using supervised machine learning. In Proceedings of the 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, USA, 11–14 December 2017. [Google Scholar]
- Dey, S. Securing Majority-Attack in Blockchain Using Machine Learning And Algorithmic Game Theory: A Proof of Work. arXiv, 2018; arXiv:1806.05477. [Google Scholar] [CrossRef]
- Harlev, M.A.; Sun Yin, H.; Langenheldt, K.C.; Mukkamala, R.; Vatrapu, R. Breaking Bad: De-Anonymising Entity Types on the Bitcoin Blockchain Using Supervised Machine Learning. In Proceedings of the 51st Hawaii International Conference on System Sciences, Waikoloa Village, HI, USA, 2–6 January 2018. [Google Scholar]
- Colianni, S.; Rosales, S.; Signorotti, M. Algorithmic Trading of Cryptocurrency Based on Twitter Sentiment Analysis. CS229 Project. 2015, pp. 1–5. Available online: http://cs229.stanford.edu/proj2015/029report.pdf (accessed on 30 August 2018).
- Kim, Y.B.; Kim, J.G.; Kim, W.; Im, J.H.; Kim, T.H.; Kang, S.J.; Kim, C.H. Predicting fluctuations in cryptocurrency transactions based on user comments and replies. PLoS ONE 2016, 11, e0161197. [Google Scholar] [CrossRef] [PubMed]
- Lu, H.K.; Yang, L.W.; Lin, P.C.; Yang, T.H.; Chen, A.N. A study on adoption of bitcoin in Taiwan: Using big data analysis of social media. In Proceedings of the 3rd International Conference on Communication and Information Processing, Tokyo, Japan, 24–26 November 2017; pp. 32–38. [Google Scholar]
- Maesa, D.D.F.; Marino, A.; Ricci, L. Detecting artificial behaviours in the Bitcoin users graph. Online Soc. Netw. Media 2017, 3, 63–74. [Google Scholar] [CrossRef]
- Jang, H.; Lee, J. An empirical study on modeling and prediction of bitcoin prices with bayesian neural networks based on blockchain information. IEEE Access 2018, 6, 5427–5437. [Google Scholar] [CrossRef]
- Nakano, M.; Takahashi, A.; Takahashi, S. Bitcoin technical trading with artificial neural network. Phys. A Stat. Mech. Appl. 2018, 510, 587–609. [Google Scholar] [CrossRef]
- McNally, S.; Roche, J.; Caton, S. Predicting the price of Bitcoin using Machine Learning. In Proceedings of the 2018 26th Euromicro International Conference on Parallel, Distributed and Network-based Processing (PDP), Cambridge, UK, 21–23 March 2018; pp. 339–343. [Google Scholar]
- Kristjanpoller, W.; Minutolo, M.C. A hybrid volatility forecasting framework integrating GARCH, artificial neural network, technical analysis and principal components analysis. Expert Syst. Appl. 2018, 109, 1–11. [Google Scholar] [CrossRef]
- Karasu, S.; Altan, A.; Sarac, Z.; Hacioglu, R. Prediction of Bitcoin prices with machine learning methods using time series data. In Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, 2–5 May 2018. [Google Scholar]
- Alessandretti, L.; ElBahrawy, A.; Aiello, L.M.; Baronchelli, A. Machine Learning the Cryptocurrency Market. arXiv, 2018; arXiv:1805.08550. [Google Scholar] [CrossRef][Green Version]
- Velankar, S.; Valecha, S.; Maji, S. Bitcoin price prediction using machine learning. In Proceedings of the 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon-si, Korea, 11–14 February 2018; pp. 144–147. [Google Scholar]
Perspective | References | Key Techniques | Application Areas |
---|---|---|---|
Security and privacy enhancement | [46,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105] | purpose-centric access model [46], secure multi-party computing [46], Ethereum blockchain [88,90,91,97,99], Hyperledger Fabric [89], hash-chain [92], heterogeneous key management [95], self-advancing goaf edge support system [96], BitTorrent [97], generalized Diffie-Hellman [98], Bitcoin chain with public key [100], supervised machine learning classification [103,104,105], algorithmic game theory [104], gradient boosting algorithm [105] | medical record access [46,88,89],personal health care data processing and monitoring [90,91],IoT security and privacy [93,94,98,99,100,102],intelligent transportation system [95],underground mines safety and productivity [96], smart city security [97], cloud computing security [101], cybercriminal entities of Bitcoin ecosystem [103,105], majority-attack prevention [104] |
Analyses and prediction | [23,106,107,108,109,110,111,112,113,114,115,116] | text classification [106,107,108], sentiment analysis [107,108], clustering heuristics [109], bayesian neural networks [110], support vector machine [23,114], GARCH [23,113], artificial neural networks [111,113], principal component analysis [113], gradient boosting decision tree [115], recurrent neural networks [115], bayesian regression and generalized linear model/random forest [116] | trading strategy advancement [106], price and/or transaction and/or return forecast [107,110,111,112,113,114,115,116], cryptocurrency adoption determinants [108], artificial users behaviour identification [109] |
© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Share and Cite
Hassani, H.; Huang, X.; Silva, E. Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data Cogn. Comput. 2018, 2, 34. https://doi.org/10.3390/bdcc2040034
Hassani H, Huang X, Silva E. Big-Crypto: Big Data, Blockchain and Cryptocurrency. Big Data and Cognitive Computing. 2018; 2(4):34. https://doi.org/10.3390/bdcc2040034
Chicago/Turabian StyleHassani, Hossein, Xu Huang, and Emmanuel Silva. 2018. "Big-Crypto: Big Data, Blockchain and Cryptocurrency" Big Data and Cognitive Computing 2, no. 4: 34. https://doi.org/10.3390/bdcc2040034