Abstract
Blockchain as emerging technology is revolutionizing several industries, especially the education industry, which has high requirements for the authenticity of data. The proposed blockchain technology realizes decentralization and time-sequence chain storage of data blocks, ensuring that the stored data blocks are not tamperable and unforgeable, and satisfy the high trust of data authenticity. However, current League Chains (such as Hyperledger Fabric) generally have problems such as low throughput and lack of indexing technology, which leads to inefficient data retrieval problems. To this end, this paper proposes a new elastic Bloom filter model that combines smart contracts. This model provides an adaptive adjustment method for Bloom filters, it can effectively reduce the false positive probability under the condition of low memory consumption and improve the efficiency of data retrieval. The experimental results based on Hyperledger Fabric show that compared with the standard Bloom filter model, the proposed model guarantees a lower false positive probability and verifies its high efficiency under data retrieval.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Smolenski, N.: Academic Credentials in an Era of Digital Decentralization (2016)
Cachin, C.: Architecture of the Hyperledger Blockchain Fabric (2016). https://www.zurich.ibm.com/dccl/papers/cachin_dccl.pdf
Patgiri, R., Nayak, S., Borgohain, S.K.: Role of bloom filter in big data research: a survey (2019)
Wang, X., Hu, Q., Zhang, Y., Zhang, G., Juan, W., Xing, C.: A kind of decision model research based on big data and blockchain in eHealth. In: Meng, X., Li, R., Wang, K., Niu, B., Wang, X., Zhao, G. (eds.) WISA 2018. LNCS, vol. 11242, pp. 300–306. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-02934-0_28
Papadimitriou, C.H.: The Serializability of Concurrent Database Updates. ACM, New York (1979)
Dickerson, T., Gazzillo, P., Herlihy, M., et al.: Adding concurrency to smart contracts. In: ACM Symposium on Principles of Distributed Computing, pp. 303–312. ACM (2017)
Siddiqi, M., All, S.T., Sivaraman, V.: Secure lightweight context-driven data logging for bodyworn sensing devices. In: International Symposium on Digital Forensic and Security, pp. 1–6. IEEE (2017)
Guan, Z.: Privacy-preserving and efficient aggregation based on blockchain for power grid communications in smart communities, cryptography and security (cs.CR). https://arxiv.org/abs/1806.01056
Dannen, C.: Introducing ethereum and solidity: foundations of cryptocurrency and blockchain programming for beginners. In: Introducing Ethereum and Solidity. Apress (2017)
Liu, A., Du, X., Li, S.: Big data access control mechanism based on block chain. J. Softw. 1–18. https://doi.org/10.13328/j.cnki.jos.005771
Nakamoto, S.: Bitcoin: a peer-to-peer electronic cash system. White Paper (2008)
Shao, Q., Jin, C., Zhang, Z., et al.: Blockchain technology: architecture and progress. J. Comput. Sci. 6(5), 691–696 (2018)
Szabo, N.: Formalizing and securing relationships on public networks. First Monday 2(9) (1997)
Alharby, M., Van Moorsel, A.: Blockchain-based smart contracts: a systematic mapping study (2017)
Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)
Hao, F., Kodialam, M., Lakshman, T.V.: Building high accuracy bloom filters using partitioned hashing. Perform. Eval. Rev. 35(1), 277–287 (2007)
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
Ma, X., Xu, L., Xu, L. (2019). Blockchain Retrieval Model Based on Elastic Bloom Filter. In: Ni, W., Wang, X., Song, W., Li, Y. (eds) Web Information Systems and Applications. WISA 2019. Lecture Notes in Computer Science(), vol 11817. Springer, Cham. https://doi.org/10.1007/978-3-030-30952-7_53
Download citation
DOI: https://doi.org/10.1007/978-3-030-30952-7_53
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30951-0
Online ISBN: 978-3-030-30952-7
eBook Packages: Computer ScienceComputer Science (R0)
