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Erschienen in:
Data-Driven Energy Efficiency Evaluation and Energy Anomaly Detection of Multi-type Enterprises Based on Energy Consumption Big Data Mining Kapitel lesen Erstes Kapitel lesen

2023 | OriginalPaper | Buchkapitel

Research on Data Watermark Tracing System in Hadoop Environment

verfasst von : Wenyu Qiao, Jiexi Wang

Erschienen in: Big Data and Security

Verlag: Springer Nature Singapore

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Abstract

The application of big data requires effective tracing of data transmission and flow processes, so as to achieve effective determination of data authenticity and security. If network data is not effectively supervised, unexpected events such as data loss, leakage or tampering will occur, resulting in network data threats and risks that cannot be traced and responded to. The traditional data tracing methods are found difficult to meet the processing needs of massive data. Hence, data tracing in Hadoop environment is considered to better deal with the risk of data loss, tampering and leakage in the process of multiple data distribution. In this paper, the Hadoop environment system and its application in the field of data watermark tracing is explored. By analyzing the data tracing model and its implementation, a data watermark tracing system in Hadoop environment is established and the tracing process is examined. The experiments are designed and the efficiency of the proposed system is validated.

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Vorheriges Kapitel A Cross-Platform Instant Messaging User Association Method Based on Supervised Learning
Nächstes Kapitel Application of RFID Tag in the Localization of Power Cable Based on Big Data
Literatur
1.
Wang, L., Zhou, G., Wang, L., Peng, Z.: Attribute level lineage and probabilistic computation of uncertain data. J. Softw. 25(04), 863–879 (2014)CrossRefMATH
2.
3.
Hondo, F., et al.: Data provenance management for bioinformatics workflows using NoSQL database systems in a cloud computing environment. In: 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 1929–1934 (2017)
4.
Gort, M.L.P., Olliaro, M., Cortesi, A.: Reducing multiple occurrences of meta-mark selection in relational data watermarking. IEEE Access 10, 62210–62231 (2022)CrossRef
5.
Chen, L., Shi, C., Yu, P., Shen, W., Gao, X., Yang, R.: Research and application of digital watermark tracing technology in data security protection of power grid. In: 2020 IEEE 3rd International Conference of Safe Production and Informatization (IICSPI), pp. 530–533 (2020)
6.
Li, D., Li, Y., Yuan, C., Chen, H., Zhang, L.: Research on private cloud platform of seed tracing based on Hadoop parallel computing. In: 2015 4th International Conference on Computer Science and Network Technology (ICCSNT), pp. 134–137 (2015)
7.
Shen, X., Zhang, Y., Wang, T., Sun, Y.: Relational Database watermarking for data tracing. In: 2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 224–231 (2020)
8.
Darwish, S.M., Selim, H.A., Elsherbiny, M.M.: Distortion free database watermarking system based on intelligent mechanism for content integrity and ownership control. J. Comput. 13(9), 1053–1066 (2018)CrossRef
9.
Dwivedi, A.K., Sharma, B.K., Vyas, A.K.: Watermarking techniques for ownership protection of relational databases. Int. J. Emerg. Technol. Adv. Eng. 4(1), 368–375 (2014)
10.
Tosh, D.K., Shetty, S., Liang, X., Kamhoua, C., Njilla, L.: Consensus protocols for blockchain-based data provenance: challenges and opportunities. In: 2017 IEEE 8th Annual Ubiquitous Computing, Electronics and Mobile Communication Conference (UEMCON), pp. 469–474 (2017)
11.
Liang, X., Shetty, S., Tosh, D., Kamhoua, C., Kwiat, K., Njilla, L.: ProvChain: a blockchain-based data provenance architecture in cloud environment with enhanced privacy and availability. In: 2017 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), pp. 468–477 (2017)
12.
Siledar, S., Tamane, S.: A distortion-free watermarking approach for verifying integrity of relational databases. In: 2020 International Conference on Smart Innovations in Design, Environment, Management, Planning and Computing (ICSIDEMPC), pp. 192–195 (2020)
13.
Kamal, M., Tariq, S.: Light-weight security and data provenance for multi-hop internet of things. IEEE Access 6, 34439–34448 (2018)CrossRef
14.
Elkhodr, M., Alsinglawi, B., Alshehri, M.: Data provenance in the internet of things. In: 2018 32nd International Conference on Advanced Information Networking and Applications Workshops (WAINA), pp. 727–731 (2018)
15.
Goyal, M., Anuranjana: Demonetization-Twitter data analysis using big data & Hadoop. In: 2019 Amity International Conference on Artificial Intelligence (AICAI), pp. 156–158 (2019)
Metadaten
Titel
Research on Data Watermark Tracing System in Hadoop Environment
verfasst von
Wenyu Qiao
Jiexi Wang
Copyright-Jahr
2023
Verlag
Springer Nature Singapore
Buch
Big Data and Security

Print ISBN: 978-981-9932-99-3

Electronic ISBN: 978-981-9933-00-6

Copyright-Jahr: 2023

DOI
https://doi.org/10.1007/978-981-99-3300-6_7

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