Data Classification and Emerging Encryption Technologies in Big Data and Cloud Computing : A Systematic Review
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This article provides a comprehensive review of recent advancements in data classification methods within the context of big data and cloud computing. As organizations increasingly rely on massive volumes of digital information, robust classification techniques have become essential, particularly for handling sensitive or confidential data. The study explores key approaches, including automated document classification and encryption-based strategies, each addressing distinct challenges related to security and efficiency. Emphasis is placed on how these methods safeguard data confidentiality, integrity, and availability critical factors in mitigating unauthorized access and cyber threats. The review also identifies pressing research gaps, such as the need for more scalable, efficient, and user-friendly classification systems that can adapt to the evolving nature of big data. The objective is to provide an in-depth overview of current practices, highlight persistent challenges, and outline promising directions for future research in this crucial field.
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[1] M. Abadi, A. Chu, I. Goodfellow, H. B. Mcmahan, I. Mironov, K. Talwar, and L. Zhang, ‘‘Deep learning with differential privacy,’’ in Proc. ACM SIGSAC Conf. Comput. Commun. Secur., Oct. 2016, pp. 308–318, doi.org/10.48550/arXiv.1607.00133.
[2] M. Choraś and M. Pawlicki, ‘‘Intrusion detection approach based onoptimised artificial neural network,’’ Neurocomputing, vol. 452, pp. 705–715, Sep. 2021, doi.org/10.1016/j.neucom.2020.07.138.
[3] A. Acar, H. Aksu, A. S. Uluagac, and M. Conti, ‘‘A survey on homomorphic encryption schemes: Theory and implementation,’’ ACM Comput. Surv., vol. 51, no. 4, pp. 1–35, 2018, doi.org/10.48550/arXiv.1704.03578.
[4] B. Li and D. Micciancio, ‘‘On the security of homomorphic encryption on approximate numbers,’’ in Advances in Cryptology—EUROCRYPT 2021 (Lecture Notes in Computer Science), vol. 12696, A. Canteaut and F. X. Standaert, Eds. Cham, Switzerland: Springer, Oct. 2021, doi: 10.1007/978- 3-030-77870-5_23.
[5] F. Boemer, A. Costache, R. Cammarota, and C. Wierzynski, ‘‘NGraphHE2: A high-throughput framework for neural network inference on encrypted data,’’ in Proc. 7th ACM Workshop Encrypted Comput. Appl. Homomorphic Cryptogr. (WAHC), 2019, pp. 45–56, doi.org/10.48550/arXiv.1908.04172.
[6] Z. Du et al., "Merge Loss Calculation Method for Highly Imbalanced Data Multiclass Classification," in IEEE Transactions on Neural Networks and Learning Systems, doi: 10.1109/TNNLS.2023.3321753.
[7] S. K. V, M. K. V, C. N. Azmea, and K. K. Vaigandla, “BCSDNCC: A Secure Blockchain SDN framework for IoT and Cloud Computing”, Int. Res. J. multidiscip. Technovation, vol. 6, no. 3, pp. 26–44, Apr. 2024, doi: 10.54392/irjmt2433.
[8] B. K. Sethi, D. Singh, S. K. Rout and S. K. Panda, "Long Short-Term Memory-Deep Belief Network based Gene Expression Data Analysis for Prostate Cancer Detection and Classification," in IEEE Access, doi: 10.1109/ACCESS.2023.3346925.
[9] M. J. Zideh, P. Chatterjee and A. K. Srivastava, "Physics-Informed Machine Learning for Data Anomaly Detection, Classification, Localization, and Mitigation: A Review, Challenges, and Path Forward," in IEEE Access, doi: 10.1109/ACCESS.2023.3347989.
[10] C. Madhu and S. M.S., "An Interpretable Fuzzy Graph Learning for Label Propagation Assisting Data Classification," in IEEE Transactions on Fuzzy Systems, doi: 10.1109/TFUZZ.2023.3323093.
[11] K. K. Vaigandla, "Communication Technologies and Challenges on 6G Networks for the Internet: Internet of Things (IoT) Based Analysis," 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM), 2022, pp. 27-31, doi: 10.1109/ICIPTM54933.2022.9753990.
[12] A. Alabdulatif, I. Khalil, and X. Yi, ‘‘Towards secure big data analytic for cloud-enabled applications with fully homomorphic encryption,’’ J. Parallel Distrib. Comput., vol. 137, pp. 192–204, Mar. 2020, doi.org/10.1016/j.jpdc.2019.10.008.
[13] J. Park, D. S. Kim and H. Lim, "Privacy-Preserving Reinforcement Learning Using Homomorphic Encryption in Cloud Computing Infrastructures," in IEEE Access, vol. 8, pp. 203564-203579, 2020, doi: 10.1109/ACCESS.2020.3036899.
[14] H. Pang and B. Wang, "Privacy-Preserving Association Rule Mining Using Homomorphic Encryption in a Multikey Environment," in IEEE Systems Journal, vol. 15, no. 2, pp. 3131- 3141, June 2021, doi: 10.1109/JSYST.2020.3001316.
[15] Li, X. Kuang, S. Lin, X. Ma, and Y. Tang, ‘‘Privacy preservationfor machine learning training and classification based on homomorphicencryption schemes,’’ Inf. Sci., vol. 526, pp. 166–179, Jul. 2020, doi.org/10.1016/j.ins.2020.03.041.
[16] A. Agarwal, M. Khari, and R. Singh, ‘‘Detection of DDOS attack using deep learning model in cloud storage application,’’ Wireless Pers. Commun., Mar. 2021, doi: 10.1007/s11277-021-08271-z.
[17] Z. Zhang, C. Li, B. B. Gupta and D. Niu, "Efficient Compressed Ciphertext Length Scheme Using Multi-Authority CP-ABE for Hierarchical Attributes," in IEEE Access, vol. 6, pp. 38273-38284, 2018, doi: 10.1109/ACCESS.2018.2854600.
[18] B. Gupta, “An efficient KP design framework of attribute-based searchable encryption for user level revocation in cloud,” Concurrency Comput. Pract. Exp., vol. 32, no. 18, 2020, Art. no. e5291, doi.org/10.1002/cpe.5291.
[19] B. Joshi, B. Joshi, A. Mishra, V. Arya, A. K. Gupta, and D. Perakovic,´ “A comparative study of privacy-preserving homomorphic encryption techniques in cloud computing,” Int. J. Cloud Appl. Comput., vol. 12, no. 1, pp. 1–11, 2022, DOI:10.4018/IJCAC.309936.
[20] H. Chen, W. Dai, M. Kim, and Y. Song, “Efficient multi-key homomorphic encryption with packed ciphertexts with application to oblivious neural network inference,” in Proc. ACM SIGSAC Conf. Comput. Commun. Security, 2019, pp. 395–412, DOI:10.1145/3319535.3363207.
[21] C. Marcolla, V. Sucasas, M. Manzano, R. Bassoli, F. H. P. Fitzek and N. Aaraj, "Survey on Fully Homomorphic Encryption, Theory, and Applications," in Proceedings of the IEEE, vol. 110, no. 10, pp. 1572-1609, Oct. 2022, doi: 10.1109/JPROC.2022.3205665.
[22] M. Ali, J. Mohajeri, M.-R. Sadeghi, and X. Liu, “A fully distributed hierarchical attributebased encryption scheme,” Theor. Comput. Sci., vol. 815, pp. 25–46, May 2020, doi.org/10.1016/j.tcs.2020.02.030.
[23] W. Xu, Y. Zhan, Z. Wang, B. Wang and Y. Ping, "Attack and Improvement on a Symmetric Fully Homomorphic Encryption Scheme," in IEEE Access, vol. 7, pp. 68373-68379, 2019, doi: 10.1109/ACCESS.2019.2917028.
[24] D. B. Rawat, R. Doku and M. Garuba, "Cybersecurity in Big Data Era: From Securing Big Data to Data-Driven Security," in IEEE Transactions on Services Computing, vol. 14, no. 6, pp. 2055-2072, 1 Nov.-Dec. 2021, doi: 10.1109/TSC.2019.2907247.
[25] F. M. Awaysheh, M. N. Aladwan, M. Alazab, S. Alawadi, J. C. Cabaleiro and T. F. Pena, "Security by Design for Big Data Frameworks Over Cloud Computing," in IEEE Transactions on Engineering Management, vol. 69, no. 6, pp. 3676-3693, Dec. 2022, doi: 10.1109/TEM.2020.3045661.
[26] G. D. Samaraweera and J. M. Chang, "Security and Privacy Implications on Database Systems in Big Data Era: A Survey," in IEEE Transactions on Knowledge and Data Engineering, vol. 33, no. 1, pp. 239-258, 1 Jan. 2021, doi: 10.1109/TKDE.2019.2929794.
[27] Sivapriya, N. ., Mohandas, R. ., & Vaigandla, K. K. . (2023). A QoS Perception Routing Protocol for MANETs Based on Machine Learning. International Journal of Intelligent Systems and Applications in Engineering, 12(1), 733–745. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/4171
[28] A. Sasikumar, L. Ravi, K. Kotecha, A. Abraham, M. Devarajan and S. Vairavasundaram, "A Secure Big Data Storage Framework Based on Blockchain Consensus Mechanism With Flexible Finality," in IEEE Access, vol. 11, pp. 56712-56725, 2023, doi: 10.1109/ACCESS.2023.3282322.
[29] R. Gupta, I. Gupta, A. K. Singh, D. Saxena and C. -N. Lee, "An IoT-Centric Data Protection Method for Preserving Security and Privacy in Cloud," in IEEE Systems Journal, vol. 17, no. 2, pp. 2445-2454, June 2023, doi: 10.1109/JSYST.2022.3218894.
[30] R. Abdillah, Z. Shukur, M. Mohd, T. S. M. Z. Murah, I. Oh and K. Yim, "Performance Evaluation of Phishing Classification Techniques on Various Data Sources and Schemes," in IEEE Access, vol. 11, pp. 38721-38738, 2023, doi: 10.1109/ACCESS.2022.3225971.
[31] A. S. Tarawneh, E. S. Alamri, N. N. Al-Saedi, M. Alauthman and A. B. Hassanat, "CTELC: A Constant-Time Ensemble Learning Classifier Based on KNN for Big Data," in IEEE Access, vol. 11, pp. 89791-89802, 2023, doi: 10.1109/ACCESS.2023.3307512.
[32] M. Dener, S. Al and G. Ok, "RFSE-GRU: Data Balanced Classification Model for Mobile Encrypted Traffic in Big Data Environment," in IEEE Access, vol. 11, pp. 21831-21847, 2023, doi: 10.1109/ACCESS.2023.3251745.
[33] Q. Liu, P. Li, W. Zhao, W. Cai, S. Yu and V. C. M. Leung, "A Survey on Security Threats and Defensive Techniques of Machine Learning: A Data Driven View," in IEEE Access, vol. 6, pp. 12103-12117, 2018, doi: 10.1109/ACCESS.2018.2805680.
[34] Y. Zhao, X. Yang, Y. Yu, B. Qin, X. Du and M. Guizani, "Blockchain-Based Auditable Privacy-Preserving Data Classification for Internet of Things," in IEEE Internet of Things Journal, vol. 9, no. 4, pp. 2468-2484, 15 Feb.15, 2022, doi: 10.1109/JIOT.2021.3097890.
[35] I. Hababeh, A. Gharaibeh, S. Nofal and I. Khalil, "An Integrated Methodology for Big Data Classification and Security for Improving Cloud Systems Data Mobility," in IEEE Access, vol. 7, pp. 9153-9163, 2019, doi: 10.1109/ACCESS.2018.2890099.
[36] Y. Zhuo, Z. Yin and Z. Ge, "Attack and Defense: Adversarial Security of Data-Driven FDC Systems," in IEEE Transactions on Industrial Informatics, vol. 19, no. 1, pp. 5-19, Jan. 2023, doi: 10.1109/TII.2022.3197190.
[37] S. Liu et al., "Model-Free Data Authentication for Cyber Security in Power Systems," in IEEE Transactions on Smart Grid, vol. 11, no. 5, pp. 4565-4568, Sept. 2020, doi: 10.1109/TSG.2020.2986704.
[38] A. Zigomitros, F. Casino, A. Solanas and C. Patsakis, "A Survey on Privacy Properties for Data Publishing of Relational Data," in IEEE Access, vol. 8, pp. 51071-51099, 2020, doi: 10.1109/ACCESS.2020.2980235.
[39] T. Shang, F. Zhang, X. Chen, J. Liu and X. Lu, "Identity-Based Dynamic Data Auditing for Big Data Storage," in IEEE Transactions on Big Data, vol. 7, no. 6, pp. 913-921, 1 Dec. 2021, doi: 10.1109/TBDATA.2019.2941882.
[40] R. Jiang, M. Shi and W. Zhou, "A Privacy Security Risk Analysis Method for Medical Big Data in Urban Computing," in IEEE Access, vol. 7, pp. 143841-143854, 2019, doi: 10.1109/ACCESS.2019.2943547.
[41] Y. Liang, D. Quan, F. Wang, X. Jia, M. Li and T. Li, "Financial Big Data Analysis and Early Warning Platform: A Case Study," in IEEE Access, vol. 8, pp. 36515-36526, 2020, doi: 10.1109/ACCESS.2020.2969039.
[42] C. Yang, X. Xu, K. Ramamohanarao and J. Chen, "A Scalable Multi-Data Sources Based Recursive Approximation Approach for Fast Error Recovery in Big Sensing Data on Cloud," in IEEE Transactions on Knowledge and Data Engineering, vol. 32, no. 5, pp. 841-854, 1 May 2020, doi: 10.1109/TKDE.2019.2895612.
[43] R. Yadav, Ritambhara, K. K. Vaigandla, G. S. P. Ghantasala, R. Singh and D. Gangodkar, "The Block Chain Technology to protect Data Access using Intelligent Contracts Mechanism Security Framework for 5G Networks," 2022 5th International Conference on Contemporary Computing and Informatics (IC3I), Uttar Pradesh, India, 2022, pp. 108-112, doi: 10.1109/IC3I56241.2022.10072740.
[44] K. K. Vaigandla, R. Karne, M. Siluveru, and M. Kesoju, “Review on blockchain technology: Architecture, characteristics, benefits, algorithms, challenges and applications,” Mesopotamian Journal of CyberSecurity, pp. 73–85, 2023, doi: 10.58496/MJCS/2023/012.
[45] S. Funde and G. Swain, "Big Data Privacy and Security Using Abundant Data Recovery Techniques and Data Obliviousness Methodologies," in IEEE Access, vol. 10, pp. 105458- 105484, 2022, doi: 10.1109/ACCESS.2022.3211304.
[46] K. R. Sollins, "IoT Big Data Security and Privacy Versus Innovation," in IEEE Internet of Things Journal, vol. 6, no. 2, pp. 1628-1635, April 2019, doi: 10.1109/JIOT.2019.2898113.
[47] S. Seo and J. -M. Chung, "Adaptive Trust Management and Data Process Time Optimization for Real-Time Spark Big Data Systems," in IEEE Access, vol. 9, pp. 156372-156379, 2021, doi: 10.1109/ACCESS.2021.3129885.
[48] S. Han, K. Han and S. Zhang, "A Data Sharing Protocol to Minimize Security and Privacy Risks of Cloud Storage in Big Data Era," in IEEE Access, vol. 7, pp. 60290-60298, 2019, doi: 10.1109/ACCESS.2019.2914862.
[49] C. Banapuram, A. C. Naik, M. K. Vanteru, V. S. Kumar, and K. K. Vaigandla, “A comprehensive survey of machine learning in healthcare: Predicting heart and liver disease, tuberculosis detection in chest X-ray images,” SSRG International Journal of Electronics and Communication Engineering, vol. 11, no. 5, pp. 155–169, 2024.
[50] X. Yang, R. Lu, K. K. R. Choo, F. Yin and X. Tang, "Achieving Efficient and Privacy- Preserving Cross-Domain Big Data Deduplication in Cloud," in IEEE Transactions on Big Data, vol. 8, no. 1, pp. 73-84, 1 Feb. 2022, doi: 10.1109/TBDATA.2017.2721444.
[51] A. D’Alconzo, I. Drago, A. Morichetta, M. Mellia and P. Casas, "A Survey on Big Data for Network Traffic Monitoring and Analysis," in IEEE Transactions on Network and Service Management, vol. 16, no. 3, pp. 800-813, Sept. 2019, doi: 10.1109/TNSM.2019.2933358.
[52] M. Ali, M. -R. Sadeghi and X. Liu, "Lightweight Revocable Hierarchical Attribute-Based Encryption for Internet of Things," in IEEE Access, vol. 8, pp. 23951-23964, 2020, doi: 10.1109/ACCESS.2020.2969957.
[53] M. Ali, M.-R. Sadeghi, X. Liu, Y. Miao, and A. V. Vasilakos, “Verifiable online/offline multikeyword search for cloud-assisted Industrial Internet of Things,” J. Inf. Security Appl., vol. 65, Mar. 2022, Art. no. 103101, doi.org/10.1016/j.jisa.2021.103101.
[54] K. Munjal and R. Bhatia, “A systematic review of homomorphic encryption and its contributions in healthcare industry,” Complex Intelligent Systems, vol. 9, pp. 3759–3786, 2023, doi: 10.1007/s40747-022-00756-z.
[55] Z. Zhang, C. Li, B. B. Gupta and D. Niu, "Efficient Compressed Ciphertext Length Scheme Using Multi-Authority CP-ABE for Hierarchical Attributes," in IEEE Access, vol. 6, pp. 38273-38284, 2018, doi: 10.1109/ACCESS.2018.2854600.
[56] K. K. Vaigandla, T. Mounika, N. Azmi, U. Urooj, K. Chenigaram, and R. K. Karne, “Investigation on machine learning towards future generation communications,” in AIP Conference Proceedings, vol. 2965, no. 1, July 2024, AIP Publishing.