On Detecting Cold Storage Transactions on Bitcoin's Blockchain
Resumo
There is a disparity between Bitcoin addresses and real-world entities: the same entity can have many addresses. In Blockchain's analysis, a common technique used for clustering addresses is to view addresses present at the input of the same transaction as a single entity. A common practice to make Bitcoin safer is the use of cold wallets. The use of cold wallets by exchanges - that control the wallets of many users - may disrupt Blockchain's current methods of analysis. In this work we define these scenarios and introduce an heuristic and an algorithm to detect these occurrences on Blockchain. We show that the data obtained using the proposed heuristic are consistent with what was expected.
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Androulaki, E., Karame, G. O., Roeschlin, M., Scherer, T., and Capkun, S. (2013). Evaluating user privacy in bitcoin. In Sadeghi, A.-R., editor, Financial Cryptography and Data Security, pages 34–51, Berlin, Heidelberg. Springer Berlin Heidelberg.
Antonopoulos, A. M. (2014). Mastering Bitcoin: Unlocking Digital Crypto-Currencies. O’Reilly Media, Inc., 1st edition.
Chuen, D. (2015). Handbook of Digital Currency: Bitcoin, Innovation, Financial Instruments, and Big Data. Elsevier Science.
Filtz, E., Polleres, A., Karl, R., and Haslhofer, B. (2017). Evolution of the bitcoin address graph. In Haber, P., Lampoltshammer, T., and Mayr, M., editors, Data Science – Analytics and Applications, pages 77–82, Wiesbaden. Springer Fachmedien Wiesbaden.
Fleder, M., Kester, M. S., and Pillai, S. (2015). Bitcoin transaction graph analysis. CoRR, abs/1502.01657.
Gini, C. (1921). Measurement of inequality of incomes. The Economic Journal, 31(121):124–126.
Harrigan, M. and Fretter, C. (2016). The unreasonable effectiveness of address clustering. In 2016 Intl IEEE Conferences on Ubiquitous Intelligence Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress (UIC/ATC/ScalCom/CBDCom/IoP/SmartWorld), pages 368–373.
Hileman, G. and Rauchs, M. (2017). Global cryptocurrency benchmarking study. Technical report, University of Cambridge, Judge Business School.
Kondor, D., Pósfai, M., Csabai, I., and Vattay, G. (2014). Do the rich get richer? An empirical analysis of the Bitcoin transaction network. PLoS ONE, 9(2).
Koshy, P., Koshy, D., and McDaniel, P. (2014). An analysis of anonymity in bitcoin using p2p network traffic. In Christin, N. and Safavi-Naini, R., editors, Financial Cryptography and Data Security, pages 469–485, Berlin, Heidelberg. Springer Berlin Heidelberg.
Liao, K., Zhao, Z., Doupe, A., and Ahn, G. J. (2016). Behind closed doors: Measurement and analysis of CryptoLocker ransoms in Bitcoin. eCrime Researchers Summit, eCrime, 2016-June:1–13.
Lorenz, M. O. (1905). Methods of measuring the concentration of wealth. Publications of the American Statistical Association, 9(70):209–219.
Maesa, D. D. F., Marino, A., and Ricci, L. (2016). Uncovering the bitcoin blockchain: An analysis of the full users graph. Proceedings - 3rd IEEE International Conference on Data Science and Advanced Analytics, DSAA 2016, pages 537–546.
McGinn, D., Birch, D., Akroyd, D., Molina-Solana, M., Guo, Y., and Knottenbelt, W. J. (2016). Visualizing Dynamic Bitcoin Transaction Patterns. Big Data, 4(2):109–119.
Meiklejohn, S., Pomarole, M., Jordan, G., Levchenko, K., McCoy, D., Voelker, G. M., and Savage, S. (2016). A fistful of bitcoins: Characterizing payments among men with no names. Commun. ACM, 59(4):86–93.
Monaco, J. V. (2015). Identifying Bitcoin users by transaction behavior. Proc.SPIE - Biometric and Surveillance Technology for Human and Activity Identification XII, page 945704.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system. Bitcoin.org.
Narayanan, A., Bonneau, J., Felten, E., Miller, A., and Goldfeder, S. (2016). Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction. Princeton University Press, Princeton, NJ, USA.
Ober, M., Katzenbeisser, S., and Hamacher, K. (2013). Structure and anonymity of the bitcoin transaction graph. Future Internet, 5(2):237–250.
Ortega, M. S. (2013). The Bitcoin Transaction Graph Anonimity. PhD thesis, Universitat Oberta de Catalunya.
Ranshous, S., Joslyn, C. A., Kreyling, S., Nowak, K., Samatova, N. F., West, C. L., and Winters, S. (2017). Exchange pattern mining in the bitcoin transaction directed hypergraph. In Financial Cryptography Workshops, volume 10323 of Lecture Notes in Computer Science, pages 248–263. Springer.
Ron, D. and Shamir, A. (2013a). How did dread pirate roberts acquire and protect his bitcoin wealth? IACR Cryptology ePrint Archive, 2013:782.
Ron, D. and Shamir, A. (2013b). Quantitative analysis of the full Bitcoin transaction graph. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7859 LNCS, pages 6–24.
Spagnuolo, M., Maggi, F., and Zanero, S. (2014). Bitiodine: Extracting intelligence from the bitcoin network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8437:457–468.
Tschorsch, F. and Scheuermann, B. (2016). Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Communications Surveys and Tutorials, 18(3):2084–2123.
Vasek, M. and Moore, T. (2015). There’s no free lunch, even using bitcoin: Tracking the popularity and profits of virtual currency scams. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 8975:44–61.
Zhao, C. (2014). Graph-based forensic investigation of Bitcoin transactions. PhD thesis, Iowa State University.
Publicado
25/10/2018
Como Citar
SENDIN, Ivan da Silva.
On Detecting Cold Storage Transactions on Bitcoin's Blockchain. In: SIMPÓSIO BRASILEIRO DE SEGURANÇA DA INFORMAÇÃO E DE SISTEMAS COMPUTACIONAIS (SBSEG), 18. , 2018, Natal.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2018
.
p. 155-166.
DOI: https://doi.org/10.5753/sbseg.2018.4250.
