Abstract
Blockchain technology is an important innovation of fintech area. It mainly consists of distributed data storage, P2P propagation, consensus mechanism and encryption algorithm. From the cryptocurrency to IoT (Internet of things), the blockchain technology has been applied to many areas. However, it faces some challenges, especially for personal privacy preservation. Though there exist some studies on the privacy issue of blockchain, it still lacks a systematic review of the privacy preserving techniques for blockchain technology. This paper focuses on some methods to protect personal private data in blockchain and newly developing areas combined with blockchain. Further, we discuss the limitation of existing techniques and future development direction.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system (2008)
Wood, G.: Ethereum: a secure decentralized generalized transaction ledger. Ethereum Project Yellow Paper, vol. 151 (2014)
Li, X.: A survey on the security of blockchain systems (2018)
Zheng, Z., Xie, S., Dai, H.-N., Wang, H.: Blockchain challenges and opportunities: a survey. Int. J. Web Grid Serv. 14(4), 352–377 (2016)
Koshy, P., Koshy, D., McDaniel, P.: An analysis of anonymity in bitcoin using P2P network traffic. In: Christin, N., Safavi-Naini, R. (eds.) FC 2014. LNCS, vol. 8437, pp. 469–485. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-45472-5_30
Kaminsky, D.: Black Ops of TCP/IP (2011). https://dankaminsky.com/2011/08/05/bo2l11/
Biryukov, A., Khovratovich, D., Pustogarov, I..: Deanonymisation of clients in Bitcoin P2P network. In: Proceedings of the 21st ACM Conference on Computer and Communications Security, pp. 15–29. ACM, New York (2014)
Ruffing, T., Moreno-Sanchez, P., Kate, A.: CoinShuffle: practical decentralized coin mixing for bitcoin. In: Kutyłowski, M., Vaidya, J. (eds.) ESORICS 2014. LNCS, vol. 8713, pp. 345–364. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11212-1_20
Bissias, G., Ozisik, A.P., Levine, B.N., et al.: Sybil‐resistant mixing for bitcoin. In: Proceedings of the 2015 ACM Workshop on Privacy in the Electronic Society, pp. 149–158. ACM, New York (2014)
Rivest, R.L., Shamir, A., Tauman, Y.: How to leak a secret. In: Boyd, C. (ed.) ASIACRYPT 2001. LNCS, vol. 2248, pp. 552–565. Springer, Heidelberg (2001). https://doi.org/10.1007/3-540-45682-1_32
Ben-Sasson, E., Chiesa, A., Tromer, E.: Succinct non—interactive zero knowledge for a von Neumann architecture. In: Proceedings of NSENIX Security Symposium, pp. 781–796. USENIX Association, Berkeley (2014)
Sodamnsure. https://blog.csdn.net/zhaiguowei/article/details/809355252. Accessed 06 July 2018
Zhang, X., Jiang, Y., Yan, Y.: A glimpse at blockchain: from the perspective of privacy (2017)
Kosba, A., Miller, A., Shi, E., Wen, Z., Papamanthou, C.: Hawk: the blockchain model of cryptography and privacy-preserving smart contracts. In: IEEE Symposium on Security and Privacy, pp. 839–858 (2016)
Fixanoid. https://github.com/jpmorganchase/quorum/blob/master/docs/raft.md. Accessed 04 Dec 2018
Microsoft Research Asia. https://zhuanlan.zhihu.com/p/28597205. Accessed 18 Aug 2017
Shamsasari. https://github.com/corda/corda. Accessed 15 Dec 2018
lzha101. https://github.com/intel/linux-sgx. Accessed 13 Nov 2018
Bing. https://zhuanlan.zhihu.com/p/37099018. Accessed 21 May 2018
Kaur, J., Kaur, K.: A fuzzy approach for an IoT-based automated employee performance appraisal. Comput. Mater. Continua 53(1), 23–36 (2017)
Dorri, A., Kanhere, S.S., Jurdak, R., Gauravaram, P.: LSB: a lightweight scalable blockchain for IoT security and privacy (2017)
Dorri, A., Kanhere, S.S.: Blockchain for IoT security and privacy: the case study of a smart home (2017)
Delfs, H., Knebl, H.: Introduction to Cryptography, vol. 2. Springer, Heidelberg (2002)
Eckhoff, D., Wagner, I.: Privacy in the smart city – applications, technologies, challenges and solutions (2017)
Qiu, J., Chai, Y., Liu, Y., Gu, Z., Li, S., Tian, Z.: Automatic non-taxonomic relation extraction from big data in smart city. IEEE Access 6, 74854–74864 (2018). https://doi.org/10.1109/access.2018.2881422
Shi, C.: A novel ensemble learning algorithm based on D-S evidence theory for IoT security. Comput. Mater. Continua 57(3), 635–652 (2018)
Xiao xi. https://blog.csdn.net/fidelhl/article/details/50520572. Accessed 14 Jan 2016
Acknowledgments
This work is funded by the National Key Research and Development Plan (Grant No. 2018YFB0803504) and the National Natural Science Foundation of China (No. U1636215).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Cui, Y., Pan, B., Sun, Y. (2019). A Survey of Privacy-Preserving Techniques for Blockchain. In: Sun, X., Pan, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2019. Lecture Notes in Computer Science(), vol 11635. Springer, Cham. https://doi.org/10.1007/978-3-030-24268-8_21
Download citation
DOI: https://doi.org/10.1007/978-3-030-24268-8_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-24267-1
Online ISBN: 978-3-030-24268-8
eBook Packages: Computer ScienceComputer Science (R0)