BSHPC: Improve Big Data Privacy Based on Blockchain and High-Performance Computing (HPC)

Albandari Alsumayt

Abstract


The vast expansion and sharp rise in data across many facets of society have made it increasingly difficult to manage big data effectively. Using traditional methods to ensure the security and privacy of users’ data is no longer sufficient. In keeping with this worry, massive data storage is still crucial. High-Performance Computing (HPC) is examined to determine the need for handling blockchain issues and protecting large data in a decentralized manner that strives for resilience. This study proposes the Big Data Storage High-Performance Computing (BSHPC) approach, which addresses big data considerations in storage management to maintain accuracy and enables the usage of blockchain. The best storage management is the primary benefit of BSHPC, as only critical data is kept on the blockchain, and other data may be kept in an off-chain database using the interplanetary file system (IPFS). Furthermore, the network's node authentication in this strategy depends on trustworthy nodes. On HPC computers, data authenticity and provenance tracking would be guaranteed, and managing large data across blockchains would be more secure. The proposed method is simulated using the Python-MPI version, and the results confirm the effectiveness of the proposed method based on performance and transactions. Moreover, the proposed method is evaluated with another study in the literature on MEC-based sharing, and it proves its effectiveness.

 

Doi: 10.28991/ESJ-2024-08-06-011

Full Text: PDF


Keywords


HPC; IOT; Blockchain; Big Data; BSHPC; Security; Privacy.

References


Payton, T., & Claypoole, T. (2023). Privacy in the age of Big data: Recognizing threats, defending your rights, and protecting your family. Rowman & Littlefield, Maryland, United States.

Vasa, J., & Thakkar, A. (2023). Deep Learning: Differential Privacy Preservation in the Era of Big Data. Journal of Computer Information Systems, 63(3), 608–631. doi:10.1080/08874417.2022.2089775.

Nathan, R., Monk, C. T., Arlinghaus, R., Adam, T., Alós, J., Assaf, M., ... & Jarić, I. (2022). Big-data approaches lead to an increased understanding of the ecology of animal movement. Science, 375(6582), eabg1780. doi:10.1126/science.abg1780.

Lin, G., Zhang, H., Song, X., & Shibasaki, R. (2023). Blockchain for location-based big data-driven services. Handbook of Mobility Data Mining, Volume 3: Mobility Data-Driven Applications, 153. doi:10.1016/B978-0-323-95892-9.00009-7.

Wang, R., Xu, C., Dong, R., Luo, Z., Zheng, R., & Zhang, X. (2023). A secured big-data sharing platform for materials genome engineering: State-of-the-art, challenges and architecture. Future Generation Computer Systems, 142, 59–74. doi:10.1016/j.future.2022.12.026.

Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., De-la-Hoz-Franco, E., & De-La-Hoz-Valdiris, E. (2022). Trends and Future Perspective Challenges in Big Data. Smart Innovation, Systems and Technologies, 253, 309–325. doi:10.1007/978-981-16-5036-9_30.

Oliveira, T. A., Oliver, M., & Ramalhinho, H. (2020). Challenges for connecting citizens and smart cities: ICT, e-governance and blockchain. Sustainability (Switzerland), 12(7), 2926. doi:10.3390/su12072926.

Krichen, M., Ammi, M., Mihoub, A., & Almutiq, M. (2022). Blockchain for Modern Applications: A Survey. Sensors, 22(14), 5274. doi:10.3390/s22145274.

Dharma Putra, G., Kang, C., Kanhere, S. S., & Won-Ki Hong, J. (2022). DeTRM: Decentralized Trust and Reputation Management for Blockchain-based Supply Chains. IEEE International Conference on Blockchain and Cryptocurrency, ICBC 2022, 1–5. doi:10.1109/ICBC54727.2022.9805565.

Shrimali, B., & Patel, H. B. (2022). Blockchain state-of-the-art: architecture, use cases, consensus, challenges and opportunities. Journal of King Saud University-Computer and Information Sciences, 34(9), 6793-6807. doi:10.1016/j.jksuci.2021.08.005.

Li, X., Jiang, P., Chen, T., Luo, X., & Wen, Q. (2020). A survey on the security of blockchain systems. Future Generation Computer Systems, 107, 841–853. doi:10.1016/j.future.2017.08.020.

Zhang, S., & Lee, J. H. (2020). Analysis of the main consensus protocols of blockchain. ICT Express, 6(2), 93–97. doi:10.1016/j.icte.2019.08.001.

Luo, Z., Qu, Z., Nguyen, T., Zeng, H., & Lu, Z. (2019). Security of HPC Systems: From a Log-analyzing Perspective. ICST Transactions on Security and Safety, 6(21), 163134. doi:10.4108/eai.19-8-2019.163134.

Usman, S., Mehmood, R., & Katib, I. (2018). Big data and HPC convergence: The cutting edge and outlook. Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 224, 11–26. doi:10.1007/978-3-319-94180-6_4.

Schwan, P. (2003). Lustre: Building a File System for 1,000-node Clusters. Proceedings of the Linux Symposium, 401–409.

Khalil Alsulbi, Maher Khemakhem, Abdullah Basuhail, Fathy Eassa, Kamal Mansur Jambi, & Khalid Almarhabi. (2021). Big Data Security and Privacy: A Taxonomy with Some HPC and Blockchain Perspectives. IJCSNS International Journal of Computer Science and Network Security, 21(7), 43–55,.

Georgiou, Y., Zhou, N., Zhong, L., Hoppe, D., Pospieszny, M., Papadopoulou, N., Nikas, K., Nikolos, O. L., Kranas, P., Karagiorgou, S., Pascolo, E., Mercier, M., & Velho, P. (2020). Converging HPC, Big Data and Cloud Technologies for Precision Agriculture Data Analytics on Supercomputers. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12321 LNCS, 368–379. doi:10.1007/978-3-030-59851-8_25.

Nystrom, N. A., Buitrago, P. A., & Blood, P. D. (2019). Bridges: Converging HPC, AI, and Big Data for Enabling Discovery. Contemporary High Performance Computing, 355–383. doi:10.1201/9781351036863-14.

Esang, M. O., Akpan, E. I. O., Jimoh, T. G., Ajibola, H. R., & Dan, E. E. (2024). Data Access Control In High-Performance Computing: Preventing Unauthorized Access To Sensitive Data In Shared Clusters. International Journal of Agribusiness and Sustainable Development Research, 1(1), 38–45.

Akhtar, A., Barati, M., Shafiq, B., Rana, O., Afzal, A., Vaidya, J., & Shamail, S. (2024). Blockchain Based Auditable Access Control For Business Processes With Event Driven Policies. IEEE Transactions on Dependable and Secure Computing, 4699 - 4716. doi:10.1109/TDSC.2024.3356811.

Zhang, S., Yan, Z., Liang, W., Li, K. C., & Di Martino, B. (2024). BCAE: A Blockchain-Based Cross Domain Authentication Scheme for Edge Computing. IEEE Internet of Things Journal, 11(13), 24035–24048. doi:10.1109/JIOT.2024.3387934.

Irawan, B., & Trihatmojo, D. (2024). Decentralized Trusted Storage of Audio-Video Log Data Based on Blockchain Technology and IPFS. International Journal of Science, Technology & Management, 5(2), 473–484. doi:10.46729/ijstm.v5i2.1084.

Al-Mamun, A., Li, T., Sadoghi, M., & Zhao, D. (2018). In-memory Blockchain: Toward Efficient and Trustworthy Data Provenance for HPC Systems. Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 3808–3813. doi:10.1109/BigData.2018.8621897.

Ølnes, S. (2016). Beyond Bitcoin enabling smart government using blockchain technology. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9820 LNCS, 253–264. doi:10.1007/978-3-319-44421-5_20.

Eyal, I., Gencer, A. E., Sirer, E. G., & Van Renesse, R. (2016). Bitcoin-NG: A scalable blockchain protocol. Proceedings of the 13th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2016, 16, 45–59.

Gilad, Y., Hemo, R., Micali, S., Vlachos, G., & Zeldovich, N. (2017). Algorand: Scaling Byzantine Agreements for Cryptocurrencies. SOSP 2017 - Proceedings of the 26th ACM Symposium on Operating Systems Principles, 51–68. doi:10.1145/3132747.3132757.

Peng, S., Bao, W., Liu, H., Xiao, X., Shang, J., Han, L., Wang, S., Xie, X., & Xu, Y. (2023). A peer-to-peer file storage and sharing system based on consortium blockchain. Future Generation Computer Systems, 141, 197–204. doi:10.1016/j.future.2022.11.010.

Meiryani, Marcelino, Rusmanto, T., Lesmana, T., Modjo, M. I., & Budiarto, A. Y. (2023). Blockchain Technology in Digitalization of Recording Accounting Transactions. Journal of Theoretical and Applied Information Technology, 101(9), 3351–3361.

Ullah, F., & Al-Turjman, F. (2023). A conceptual framework for blockchain smart contract adoption to manage real estate deals in smart cities. Neural Computing and Applications, 35(7), 5033–5054. doi:10.1007/s00521-021-05800-6.

Shah, M., Shaikh, M., Mishra, V., & Tuscano, G. (2020). Decentralized Cloud Storage Using Blockchain. Proceedings of the 4th International Conference on Trends in Electronics and Informatics, ICOEI 2020, 384–389. doi:10.1109/ICOEI48184.2020.9143004.

Al-Mamun, A., Li, T., Sadoghi, M., Jiang, L., Shen, H., Zhao, D., & Shen, H.-I. (2019). HPChain: An MPI-Based Blockchain Framework for Data Fidelity in High-Performance Computing Systems. ACM Reference Format, 17–19.

Usman, S., Mehmood, R., & Katib, I. (2020). Big data and HPC convergence for smart infrastructures: A review and proposed architecture. Smart Infrastructure and Applications: Foundations for Smarter Cities and Societies, 561-586. doi:10.1007/978-3-030-13705-2_23.

Wang, J., Xu, C., Zhang, J., & Zhong, R. (2022). Big data analytics for intelligent manufacturing systems: A review. Journal of Manufacturing Systems, 62, 738–752. doi:10.1016/j.jmsy.2021.03.005.

Nanayakkara, S., Rodrigo, M. N. N., Perera, S., Weerasuriya, G. T., & Hijazi, A. A. (2021). A methodology for selection of a Blockchain platform to develop an enterprise system. Journal of Industrial Information Integration, 23, 100215. doi:10.1016/j.jii.2021.100215.

Aniello, L., Baldoni, R., Gaetani, E., Lombardi, F., Margheri, A., & Sassone, V. (2017). A Prototype Evaluation of a Tamper-Resistant High Performance Blockchain-Based Transaction Log for a Distributed Database. Proceedings - 2017 13th European Dependable Computing Conference, EDCC 2017, 151–154. doi:10.1109/EDCC.2017.31.

Al-Mamun, A., Yan, F., & Zhao, D. (2021). SciChain: Blockchain-enabled lightweight and efficient data provenance for reproducible scientific computing. Proceedings - International Conference on Data Engineering, 2021-April, 1853–1858. doi:10.1109/ICDE51399.2021.00166.

Rahman, M. A., Rashid, M. M., Shamim Hossain, M., Hassanain, E., Alhamid, M. F., & Guizani, M. (2019). Blockchain and IoT-Based Cognitive Edge Framework for Sharing Economy Services in a Smart City. IEEE Access, 7, 18611–18621. doi:10.1109/ACCESS.2019.2896065.

Stergiou, C. L., Psannis, K. E., & Gupta, B. B. (2022). InFeMo: Flexible Big Data Management Through a Federated Cloud System. ACM Transactions on Internet Technology, 22(2), 1–22. doi:10.1145/3426972.

Zhou, Q. (2022). A Study on Human Transiting Based on Big Data and Web Semantics: Distinguishment and Detection. International Journal on Semantic Web and Information Systems, 18(1), 1–18,. doi:10.4018/IJSWIS.310055.

Saba, T., Haseeb, K., Rehman, A., & Jeon, G. (2024). Blockchain-Enabled Intelligent IoT Protocol for High-Performance and Secured Big Financial Data Transaction. IEEE Transactions on Computational Social Systems, 11(2), 1667–1674. doi:10.1109/TCSS.2023.3268592.

Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges. IEEE Access, 5, 5247–5261. doi:10.1109/ACCESS.2017.2689040.

Talebkhah, M., Sali, A., Marjani, M., Gordan, M., Hashim, S. J., & Rokhani, F. Z. (2021). IoT and Big Data Applications in Smart Cities: Recent Advances, Challenges, and Critical Issues. IEEE Access, 9, 55465–55484. doi:10.1109/ACCESS.2021.3070905.

Weitzenboeck, E. M., Lison, P., Cyndecka, M., & Langford, M. (2022). The GDPR and unstructured data: is anonymization possible? International Data Privacy Law, 12(3), 184–206. doi:10.1093/idpl/ipac008.

N. Sangeeta, & Nam, S. Y. (2023). Blockchain and Interplanetary File System (IPFS)-Based Data Storage System for Vehicular Networks with Keyword Search Capability. Electronics (Switzerland), 12(7), 1545. doi:10.3390/electronics12071545.

Wendl, M., Doan, M. H., & Sassen, R. (2023). The environmental impact of cryptocurrencies using proof of work and proof of stake consensus algorithms: A systematic review. Journal of Environmental Management, 326, 116530. doi:10.1016/j.jenvman.2022.116530.

Sidhanta, S., Mukhopadhyay, S., & Golab, W. (2019). Dyn-YCSB: Benchmarking adaptive frameworks. In Proceedings - 2019 IEEE World Congress on Services, SERVICES 2019, 2642, 392–393. doi:10.1109/SERVICES.2019.00119.

Øvrelid, E., Bygstad, B., & Thomassen, G. (2021). TSD: A research platform for sensitive data. Procedia Computer Science, 181, 127–134. doi:10.1016/j.procs.2021.01.112.

Singh, A. K., & Sharma, S. D. (2019). High performance computing (HPC) data center for information as a service (IaaS) security checklist: Cloud data governance. Webology, 16(2), 83–96. doi:10.14704/web/v16i2/a192.


Full Text: PDF

DOI: 10.28991/ESJ-2024-08-06-011

Refbacks

  • There are currently no refbacks.