Research on Privacy Protection Based on Joint Learning in Power Industry Big Data Analysis

Authors

  • Feilu Hang Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China
  • Linjiang Xie Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China
  • Zhenhong Zhang Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China
  • Wei Guo Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China
  • Hanruo Li Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

DOI:

https://doi.org/10.13052/dgaej2156-3306.3759

Keywords:

Big data, blockchain joint learning process, power industry, privacy protection

Abstract

In the era of big data, protecting the privacy of smart grid data is critical in ensuring the integrity and confidentiality of that data. Utilizing large amounts of energy data to gain insight into electricity consumers’ consumption patterns helps develop power supply strategies. This article presents a Big Data-assisted Joint learning process (BDA-JLP), taking data security issues posed by big data in the electric power industry into consideration for privacy protection using K-anonymity and L-diversity as a foundation. A blockchain with JLP electric utility investigation is being conducted, part of the existing trading model split into phases. To begin, an attribute is chosen to categorize the input database. The comparable class number K and sensitive attribute value category L are limited by the number of original predecessors in the source data table, simplifying the calculation. A mathematical equation is then developed to determine the distance between first cousins multiplied by their combined weight. Linear and clustering with binary K are used to categorize data tables. Cluster and generalize initial data sets, considering how the attribute values’ internal range changes. The asymmetric encryption method uses two distinct keys for encryption and decryption ensuring that the blockchain system is completely secure. Simulated data show that the BDA-JLP mechanism proposed here has a privacy ratio of 98.3 percent, scalability of 97.0%, improved data management and data protection ratio of 98.2 percent, customer satisfaction ratio of 98.4 percent, and a low energy consumption ratio of 23.9% when compared to other methods currently available.

Author Biographies

Feilu Hang, Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

Feilu Hang was born in 1984 in Zhaotong, Yunnan province, China. Graduated from Yunnan University with a master’s degree in system Analysis and integration. At present, I am working in equipment management Department of information center of Yunnan Power Grid Co., LTD. His research interest covers network and information security.

Linjiang Xie, Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

Linjiang Xie was born in 1985 in Qujing, Yunnan Province, China. Graduated from Yunnan University with a bachelor’s degree in information security. At present, I am working in equipment management Department of information center of Yunnan Power Grid Co., LTD. His research interest is network security operation.

Zhenhong Zhang, Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

Zhenhong Zhang, born in Qujing city, Yunnan Province, China in 1989, graduated from Beijing University of Posts and Telecommunications with a master’s degree in computer technology. Currently, he is working in the equipment management department of information Center of Yunnan Power Grid Co., LTD., and his research direction is information system operation and maintenance.

Wei Guo, Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

Wei Guo, born in 1986 in Kunming, Yunnan Province, China, graduated from Chongqing University of Posts and Telecommunications with a bachelor’s degree in Information Management and Information System. Currently, he is working in the equipment management department of information Center of Yunnan Power Grid Co., LTD. His research direction is network and network security operation and peacekeeping management.

Hanruo Li, Information Center of Yunnan Power Grid Co., Ltd, Yunnan 650000, China

Hanruo Li born in 1991 in Zhaotong, Yunnan, China, graduated from Fuzhou University with a bachelor’s degree in network engineering. Currently, he is working in the equipment management Department of the information Center of Yunnan Power Grid Co., LTD. His research direction is network security operation and maintenance.

References

Gao, J., Wang, H., and Shen, H. (2020, May). Smartly handling renewable energy instability in supporting a cloud datacenter. In 2020 IEEE international parallel and distributed processing symposium (IPDPS) (pp. 769–778). IEEE.

Saravanan, V., Alagan, A., and Woungang, I. (2018). Big data in massive parallel processing: A multi-core processors perspective. In Handbook of Research on Big Data Storage and Visualization Techniques (pp. 276–302). IGI Global.

Manogaran, G., and Lopez, D. (2018). Spatial cumulative sum algorithm with big data analytics for climate change detection. Computers & Electrical Engineering, 65, 207–221.

Nguyen, T. N., Liu, B. H., Nguyen, N. P., and Chou, J. T. (2020, June). Cyber security of smart grid: attacks and defenses. In ICC 2020-2020 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.

Chen, J., Ramanathan, L., and Alazab, M. (2021). Holistic big data integrated artificial intelligent modeling to improve privacy and security in data management of smart cities. Microprocessors and Microsystems, 81, 103722.

Manogaran, G., and Lopez, D. (2018). Spatial cumulative sum algorithm with big data analytics for climate change detection. Computers & Electrical Engineering, 65, 207–221.

Babu, D. V., Saravanan, V., Kumar, P., and Singh, S. (2015). Automated robotic receptionist with embedded touch screen. Journal of Chemical and Pharmaceutical Sciences, 415–417.

Nguyen, T. G., Phan, T. V., Hoang, D. T., Nguyen, T. N., and So-In, C. (2020, December). Efficient SDN-Based Traffic Monitoring in IoT Networks with Double Deep Q-Network. In International Conference on Computational Data and Social Networks (pp. 26–38). Springer, Cham.

Gao, J., Wang, H., and Shen, H. (2020). Task failure prediction in cloud data centers using deep learning. IEEE Transactions on Services Computing.

Amudha, G., and Narayanasamy, P. (2018). Distributed location and trust based replica detection in wireless sensor networks. Wireless Personal Communications, 102(4), 3303–3321.

Nguyen, T. N., Liu, B. H., Nguyen, N. P., and Chou, J. T. (2020, June). Cyber security of smart grid: attacks and defenses. In ICC 2020–2020 IEEE International Conference on Communications (ICC) (pp. 1–6). IEEE.

Shakeel, P. M., Baskar, S., Fouad, H., Manogaran, G., Saravanan, V., and Xin, Q. (2020). Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol. Mobile Networks and Applications, 1–12.

Fenil, E., Manogaran, G., Vivekananda, G. N., Thanjaivadivel, T., Jeeva, S., and Ahilan, A. (2019). Real time violence detection framework for football stadium comprising of big data analysis and deep learning through bidirectional LSTM. Computer Networks, 151, 191–200.

Amudha, G., Jayasri, T., Saipriya, K., Shivani, A., and Praneetha, C. H. Behavioural Based Online Comment Spammers in Social Media.

Manogaran, G., Baskar, S., Hsu, C. H., Kadry, S. N., Sundarasekar, R., Kumar, P. M., and Muthu, B. A. (2020). FDM: Fuzzy-optimized Data Management Technique for Improving Big Data Analytics. IEEE Transactions on Fuzzy Systems.

Shakeel, P. M., Baskar, S., Fouad, H., Manogaran, G., Saravanan, V., and Xin, Q. (2020). Creating Collision-Free Communication in IoT with 6G Using Multiple Machine Access Learning Collision Avoidance Protocol. Mobile Networks and Applications, 1–12.

Manogaran, G., Baskar, S., Hsu, C. H., Kadry, S. N., Sundarasekar, R., Kumar, P. M., and Muthu, B. A. (2020). FDM: Fuzzy-optimized Data Management Technique for Improving Big Data Analytics. IEEE Transactions on Fuzzy Systems.

Manimuthu, A., Dharshini, V., Zografopoulos, I., Priyan, M. K., and Konstantinou, C. (2021). Contactless Technologies for Smart Cities: Big Data, IoT, and Cloud Infrastructures. SN Computer Science, 2(4), 1–24.

Abiad, M., Kadry, S., Ionescu, S., and Niculescu, A. (2019). Customers’ Perception of Telecommunication Services. FAIMA Business & Management Journal, 7(2), 51–62.

Sweeney, L. (2002). Achieving k-anonymity privacy protection using generalization and suppression. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 10(05), 571–588.

Hou, J., Li, Q., Cui, S., Meng, S., Zhang, S., Ni, Z., and Tian, Y. (2020). Low-cohesion differential privacy protection for industrial internet. The Journal of Supercomputing, 76(11), 8450–8472.

Abowd, J. M., and Schmutte, I. M. (2019). An economic analysis of privacy protection and statistical accuracy as social choices. American Economic Review, 109(1), 171–202.

Bäck, I., and Kohtamäki, M. (2016). Joint learning in innovative R&D collaboration. Industry and innovation, 23(1), 62–86.

Tariq, N., Asim, M., Al-Obeidat, F., Zubair Farooqi, M., Baker, T., Hammoudeh, M., and Ghafir, I. (2019). The security of big data in fog-enabled IoT applications including blockchain: A survey. Sensors, 19(8), 1788.

Fang, W., Wen, X. Z., Zheng, Y., and Zhou, M. (2017). A survey of big data security and privacy preserving. IETE Technical Review, 34(5), 544–560.

Shin, D. H., and Choi, M. J. (2015). Ecological views of big data: Perspectives and issues. Telematics and Informatics, 32(2), 311–320.

Liu, Y., Garg, S., Nie, J., Zhang, Y., Xiong, Z., Kang, J., and Hossain, M. S. (2020). Deep anomaly detection for time-series data in industrial iot: a communication-efficient on-device federated learning approach. IEEE Internet of Things Journal, 8(8), 6348–6358.

Qi, L., Hu, C., Zhang, X., Khosravi, M. R., Sharma, S., Pang, S., and Wang, T. (2020). Privacy-aware data fusion and prediction with spatial-temporal context for smart city industrial environment. IEEE Transactions on Industrial Informatics, 17(6), 4159–4167.

Büyüközkan, G., Havle, C. A., and Feyzioğlu, O. (2020). A new digital service quality model and its strategic analysis in aviation industry using interval-valued intuitionistic fuzzy AHP. Journal of Air Transport Management, 86, 101817.

Liu, Y., Zhang, J., and Zhan, J. (2021). Privacy protection for fog computing and the internet of things data based on blockchain. Cluster Computing, 24(2), 1331–1345.

Wang, F., Yang, N., Shakeel, P. M., and Saravanan, V. (2021). Machine learning for mobile network payment security evaluation system. Transactions on Emerging Telecommunications Technologies, e4226.

Xue, M., Xiu, G., Saravanan, V., and Montenegro-Marin, C. E. (2020). Cloud computing with AI for banking and e-commerce applications. The Electronic Library.

Saravanan, V., Nuneviller, M., Pillai, A. S., and Anpalagan, A. (2020). Foundation of Big Data and Internet of Things: Applications and Case Study. Securing IoT and Big Data, 1–14.

Published

2022-07-01

How to Cite

Hang, F. ., Xie, L. ., Zhang, Z. ., Guo, W. ., & Li, H. . (2022). Research on Privacy Protection Based on Joint Learning in Power Industry Big Data Analysis. Distributed Generation &Amp; Alternative Energy Journal, 37(05), 1493–1526. https://doi.org/10.13052/dgaej2156-3306.3759

Issue

Section

Articles