A Secure Enterprise Application Framework for Privacy-Preserving Data Processing with Integrated Master Data Management
DOI:
https://doi.org/10.63282/3050-9416.IJAIBDCMS-V5I2P121Keywords:
Privacy-Preserving Computing, Master Data Management, Secure Framework, Enterprise Applications, Data Governance, Differential Privacy, Homomorphic EncryptionAbstract
The fact that enterprise data has been growing exponentially and the growing regulatory oversight has required the creation of data processing models that ensure security and privacy. Nowadays, organizations deal with heterogeneous data that include structured data, semi-structured data and unstructured data, and greatly may exist within cloud and on-premises environments. Although Master Data Management (MDM) systems are designed to maintain consistency, accuracy, and control over the key business data, it is a challenge of which integrating them with secure data processing mechanisms. This paper also suggests a detailed Secure Enterprise Application Framework which will combine privacy-sensitive data processing methods with strong Master Data Management. The structure is aimed at the solutions of main issues e.g. data confidentiality, integrity, availability, compliance and interoperability. The new structure will use cryptographic methods, such as homomorphic encryption and secure multi-party computation to allow processing of data without revealing sensitive data. Also, the use of differential privacy to ensure that individual-level information is not leaked to attack inference has been included. The architecture is designed based on a layered architecture that includes data ingestion, security enforcement, MDM integration, processing engine and governance layers. All the layers are scalable and have been developed keeping in mind modularity so as to integrate easily with the already existing enterprise systems. One of the contributions of this work is the integration of intelligent Master Data Governance module guaranteeing the data consistency and lineage tracking with the strict privacy guarantees. Role-based access control (RBAC) and attribute-based access control (ABAC) models are also incorporated in the framework to implement fine-grained access policies. Audit trails are built upon blockchain to maximize the transparent and traceability of the data functioning. Performance, scalability and security robustness are evaluated by conducting simulated enterprise datasets through experimental evaluation. It has been shown that the proposed framework can ensure high data privacy with little performance loss. Compared to the traditional enterprise data management systems, comparative analysis indicates that there are improvements in data accuracy, compliance adherence and operational efficiency. This paper has concluded by recommending the implementation of privacy-preserving practices in conjunction with Master Data Management as a feasible solution to the current day enterprise that needs security and assurance, compliance and efficiency in its data processing systems. The given model is especially applicable to the field of healthcare, finance, and government in which the sensitivity of data and regulatory demands are the top priority.
References
1. Tran, H. T., Huynh, T. D., & others. (2019). Privacy-preserving big data analytics: A comprehensive survey. Journal of Parallel and Distributed Computing, 134, 207–218. https://doi.org/10.1016/j.jpdc.2019.08.007
2. Yang, C., Huang, Q., Li, Z., Liu, K., & Hu, F. (2017). Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10(1), 13-53.
3. Garg, A., Popli, R., & Sarao, B. S. (2021, January). Growth of digitization and its impact on big data analytics. In IOP conference series: materials science and engineering (Vol. 1022, No. 1, p. 012083). IOP Publishing.
4. Berson, A., & Dubov, L. (2007). Master data management and customer data integration for a global enterprise. McGraw-Hill, Inc..
5. Fung, B. C., Wang, K., Chen, R., & Yu, P. S. (2010). Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys (Csur), 42(4), 1-53.
6. Chen, B. C., Kifer, D., LeFevre, K., & Machanavajjhala, A. (2009). Privacy-preserving data publishing. Foundations and trends in databases, 2(1-2), 1-167.
7. Gomes, J. F., Iivari, M., Ahokangas, P., Isotalo, L., & Niemelä, R. (2017). Cybersecurity Business Models for IoT-Mobile Device Management Services in Futures Digital Hospitals. Journal of ICT Standardization, 5(1), 107-128.
8. Šprem, Š., Tomažin, N., Matečić, J., & Horvat, M. (2024). Building advanced web applications using data ingestion and data processing tools. Electronics, 13(4), 709.
9. Koo, J., Kang, G., & Kim, Y. G. (2020). Security and privacy in big data life cycle: A survey and open challenges. Sustainability, 12(24), 10571.
10. Gentry, C. (2009). A fully homomorphic encryption scheme. Stanford university.
11. Dwork, C., & Roth, A. (2014). The algorithmic foundations of differential privacy. Foundations and trends® in theoretical computer science, 9(3-4), 211-487.
12. Rivest, R. L., Adleman, L., & Dertouzos, M. L. (1978). On data banks and privacy homomorphisms. Foundations of secure computation, 4(11), 169-180.
13. Sweeney, L. (2002). k-anonymity: A model for protecting privacy. International journal of uncertainty, fuzziness and knowledge-based systems, 10(05), 557-570.
14. Li, T., Li, N., Zhang, J., & Molloy, I. (2010). Slicing: A new approach for privacy preserving data publishing. IEEE transactions on knowledge and data engineering, 24(3), 561-574.
15. Otto, B. (2012). How to design the master data architecture: Findings from a case study at Bosch. International journal of information management, 32(4), 337-346.
16. Loshin, D. (2010). Master data management. Morgan Kaufmann.
17. Dreibelbis, A. (2008). Enterprise master data management: an SOA approach to managing core information. Pearson Education India.
18. Batini, C., & Scannapieco, M. (2016). Data and information quality. Cham, Switzerland: Springer International Publishing, 63.
19. Agrawal, R., & Srikant, R. (2000, May). Privacy-preserving data mining. In Proceedings of the 2000 ACM SIGMOD international conference on Management of data (pp. 439-450).
20. Kantarcioglu, M., & Clifton, C. (2004). Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE transactions on knowledge and data engineering, 16(9), 1026-1037.
21. Zhou, B., & Pei, J. (2008, April). Preserving privacy in social networks against neighborhood attacks. In 2008 IEEE 24th International Conference on Data Engineering (pp. 506-515). IEEE.
22. Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS quarterly, 36(4), 1165-1188.
