Abstract
Many central banks, as well as blockchain systems, are looking into distributed versions of interbank payment systems, in particular the netting procedure. When executed in a distributed manner this presents a number of privacy problems. This paper studies a privacy-preserving netting protocol to solve the gridlock resolution problem in such Real Time Gross Settlement systems. Our solution utilizes Multi-party Computation and is implemented in the SCALE MAMBA system, using Shamir secret sharing scheme over three parties in an actively secure manner. Our experiments show that, even for large throughput systems, such a privacy-preserving operation is often feasible.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
The algorithm from [6] removes just one transaction of a source with a negative balance at this point, but it is equivalent to removing one transaction from each source which has a negative balance.
- 2.
- 3.
- 4.
References
Aspembitova, A., Feng, L., Melnikov, V., Chew, L.Y.: Fitness preferential attachment as a driving mechanism in bitcoin transaction network. PLoS One 14, e0219346 (2019)
Abbe, E., Khandani, A.E., Lo, A.W.: Privacy-preserving methods for sharing financial risk exposures. CoRR abs/1111.5228 (2011)
Aly, A., et al.: SCALE and MAMBA v1.14: documentation (2021). https://homes.esat.kuleuven.be/~nsmart/SCALE/Documentation.pdf
Asharov, G., Balch, T.H., Polychroniadou, A., Veloso, M.: Privacy-preserving dark pools. In: Seghrouchni, A.E.F., Sukthankar, G., An, B., Yorke-Smith, N. (eds.) Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2020, Auckland, New Zealand, 9–13, May 2020, pp. 1747–1749. International Foundation for Autonomous Agents and Multiagent Systems (2020)
Bag, S., Hao, F., Shahandashti, S.F., Ray, I.G.: SEAL: sealed-bid auction without auctioneers. IEEE Trans. Inf. Forensics Secur. 15, 2042–2052 (2020)
Bech, M., Soramaki, K.: Gridlock resolution in payment systems. Danmarks Nationalbank Monetary Review, July (2001)
Bogdanov, D., Talviste, R., Willemson, J.: Deploying secure multi-party computation for financial data analysis. In: Keromytis, A.D. (ed.) FC 2012. LNCS, vol. 7397, pp. 57–64. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32946-3_5
Bogetoft, P., et al.: Secure multiparty computation goes live. In: Dingledine, R., Golle, P. (eds.) FC 2009. LNCS, vol. 5628, pp. 325–343. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-03549-4_20
Bogetoft, P., Damgård, I., Jakobsen, T., Nielsen, K., Pagter, J., Toft, T.: A practical implementation of secure auctions based on multiparty integer computation. In: Di Crescenzo, G., Rubin, A. (eds.) FC 2006. LNCS, vol. 4107, pp. 142–147. Springer, Heidelberg (2006). https://doi.org/10.1007/11889663_10
Boss, M., Elsinger, H., Summer, M., Thurner, S.: Network topology of the interbank market. Quant. Financ. 4(6), 677–684 (2004)
Byrd, D., Polychroniadou, A.: Differentially private secure multi-party computation for federated learning in financial applications. In: ICAIF 2020: The First ACM International Conference on AI in Finance, New York, NY, USA, 15–16, October 2020, pp. 16:1–16:9 (2020)
Cao, S., Yuan, Y., De Caro, A., Nandakumar, K., Elkhiyaoui, K., Hu, Y.: Decentralized privacy-preserving netting protocol on blockchain for payment systems. In: Bonneau, J., Heninger, N. (eds.) FC 2020. LNCS, vol. 12059, pp. 137–155. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-51280-4_9
Cartlidge, J., Smart, N.P., Alaoui, Y.T.: Multi-party computation mechanism for anonymous equity block trading: a secure implementation of turquoise plato uncross. Cryptology ePrint Archive, Report 2020/662 (2020). https://eprint.iacr.org/2020/662
Cartlidge, J., Smart, N.P., Talibi Alaoui, Y.: MPC joins the dark side. In: Galbraith, S.D., Russello, G., Susilo, W., Gollmann, D., Kirda, E., Liang, Z. (eds.) ASIACCS 2019: 14th ACM Symposium on Information, Computer and Communications Security, pp. 148–159. ACM Press, Auckland, New Zealand 9–12, July 2019
Catrina, O., de Hoogh, S.: Improved primitives for secure multiparty integer computation. In: Garay, J.A., De Prisco, R. (eds.) SCN 2010. LNCS, vol. 6280, pp. 182–199. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-15317-4_13
Catrina, O., Saxena, A.: Secure computation with fixed-point numbers. In: Sion, R. (ed.) FC 2010. LNCS, vol. 6052, pp. 35–50. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14577-3_6
Chapman, J., ans Scott Hendry, R.G., McCormack, A., McMahon, W.: Project jasper: are distributed wholesale payment systems feasible yet? https://www.finextra.com/finextra-downloads/newsdocs/fsr-june-2017-chapman.pdf
Cohen, R., Havlin, S., Ben-Avraham, D.: Structural Properties of Scale-free Networks, pp. 85–110. Wiley, Hoboken (2003)
Cozzo, D., Smart, N.P., Alaoui, Y.T.: Secure fast evaluation of iterative methods: with an application to secure pagerank. In: Paterson, K.G. (ed.) CT-RSA 2021. LNCS, vol. 12704, pp. 1–25. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-75539-3_1
Damgård, I., Fitzi, M., Kiltz, E., Nielsen, J.B., Toft, T.: Unconditionally secure constant-rounds multi-party computation for equality, comparison, bits and exponentiation. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 285–304. Springer, Heidelberg (2006). https://doi.org/10.1007/11681878_15
ECB: Target 2. https://www.ecb.europa.eu/paym/target/target2/html/index.en.html
European Central Bank and Bank of Japan: Payment systems: liquidity saving mechanisms in a distributed ledger environment. https://www.ecb.europa.eu/pub/pdf/other/ecb.stella_project_report_september_2017.pdf
Galbiati, M., Soramaki, K.: Liquidity-saving mechanisms and bank behaviour. Bank of England, Bank of England working papers, July 2010
Guntzer, M.M., Jungnickel, D., Leclerc, M.: Efficient algorithms for the clearing of interbank payments. Eur. J. Oper. Res. 106(1), 212–219 (1998)
Hastings, M., Falk, B., Tsoukalas, G.: Privacy-preserving network analytics, August 2020
Inaoka, H., Ninomiya, T., Taniguchi, K., Shimizu, T., Takayasu, H.: Fractal Network derived from banking transaction - an analysis of network structures formed by financial institutions. Bank of Japan Working Paper Series 04-E-4, Bank of Japan, April 2004
Keller, M., Scholl, P.: Efficient, oblivious data structures for MPC. In: Sarkar, P., Iwata, T. (eds.) ASIACRYPT 2014. LNCS, vol. 8874, pp. 506–525. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45608-8_27
Monetary Authority of Singapore: Project Ubin: Cntral bank digital money using distributed ledger technology. https://www.mas.gov.sg/schemes-and-initiatives/Project-Ubin
Parkes, D.C., Rabin, M.O., Shieber, S.M., Thorpe, C.: Practical secrecy-preserving, verifiably correct and trustworthy auctions. Electron. Commer. Res. Appl. 7(3), 294–312 (2008)
Sangers, A., et al.: Secure multiparty pagerank algorithm for collaborative fraud detection. In: Goldberg, I., Moore, T. (eds.) FC 2019. LNCS, vol. 11598, pp. 605–623. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32101-7_35
Shafransky, Y.M., Doudkin, A.A.: An optimization algorithm for the clearing of interbank payments. Eur. J. Oper. Res. 171(3), 743–749 (2006)
Smart, N.P., Wood, T.: Error detection in monotone span programs with application to communication-efficient multi-party computation. In: Matsui, M. (ed.) CT-RSA 2019. LNCS, vol. 11405, pp. 210–229. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-12612-4_11
Soramaki, K., Bech, M.L., Arnold, J., Glass, R.J., Beyeler, W.E.: The topology of interbank payment flows. Phys. A Stat. Mech. Appl. 379(1), 317–333 (2007)
Soramaki, K., Cook, S.: Sinkrank: an algorithm for identifying systemically important banks in payment systems. Econ. Open-Access, Open-Assess. E-J. 7, 2013–2028 (2013)
Wang, X., Xu, X., Feagan, L., Huang, S., Jiao, L., Zhao, W.: Inter-bank payment system on enterprise blockchain platform. In: 11th IEEE International Conference on Cloud Computing, CLOUD 2018, San Francisco, CA, USA, 2–7, July 2018, pp. 614–621. IEEE Computer Society (2018)
Yao, A.C.C.: How to generate and exchange secrets (extended abstract). In: 27th Annual Symposium on Foundations of Computer Science, pp. 162–167. IEEE Computer Society Press, Toronto, Ontario, Canada 27–29, October 1986
Acknowledgments
We would like to thank Cedric Humbert of the European Central Bank for suggesting we look into this problem, and answering various questions we had along the way. This work has been supported in part by ERC Advanced Grant ERC-2015-AdG-IMPaCT, by the FWO under an Odysseus project GOH9718N, and by CyberSecurity Research Flanders with reference number VR20192203.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Atapoor, S., Smart, N.P., Alaoui, Y.T. (2022). Private Liquidity Matching Using MPC. In: Galbraith, S.D. (eds) Topics in Cryptology – CT-RSA 2022. CT-RSA 2022. Lecture Notes in Computer Science(), vol 13161. Springer, Cham. https://doi.org/10.1007/978-3-030-95312-6_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-95312-6_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-95311-9
Online ISBN: 978-3-030-95312-6
eBook Packages: Computer ScienceComputer Science (R0)