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Secure Data Integration Systems: Framework for Enhanced Security and Privacy in Decision-Making

This research presents a framework for creating Secure Data Integration Systems (DIS) that focus on preventing unintentional disclosure of private information through secure design principles. By integrating security requirements throughout the development process, the system aims to product accurate results essential for decision-making and disaster recovery. The findings are supported by extensive literature and case studies, emphasizing the importance of security, privacy, and trust in data integration.

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Secure Data Integration Systems: Framework for Enhanced Security and Privacy in Decision-Making

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  1. SecureDIS: a Framework for Secure Data Integration Systems Fatimah Akeel fya1g12@soton.ac.uk Supervisors: Dr. Gary B. Wills & Dr. Andrew Gravell School of Electronics and Computer Science, ESS Group

  2. SARS Katrina The use of intensive amount of data creates the so called data integration systems

  3. A Scenario of Data Integration System (DIS)

  4. Unintentional disclosure of private information caused by system design. Security Privacy Trust

  5. 1 2 Build a DIS to be secure by design 3 Focus on disclosure of private data

  6. The Goal is to: • Create a secure and reliable DIS that Produce accurate results, used in decision making and disaster recovery. Which is achieved by having security requirements propagate through the development

  7. Published Completed

  8. M. Lenzerini, “Data integration: A theoretical perspective,” in Proceedings of the 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, 2002, pp. 233–246. • A. Calì, D. Calvanese, G. D. Giacomo, and M. Lenzerini, “Data integration under integrity constraints,” Adv. Inf. Syst. Eng., pp. 262–279, 2006. • C. Clifton, M. Kantarcioǧlu, A. Doan, G. Schadow, J. Vaidya, A. Elmagarmid, and D. Suciu, “Privacy-preserving data integration and sharing,” in Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery - DMKD ’04, 2004, p. 19. • K. Pasierb, T. Kajdanowicz, and P. Kazienko, “Privacy-preserving data mining, sharing and publishing,” J. Med. Informatics Technol., vol. 18, 2011. • M. Haddad, M.-S. Hacid, and R. Laurini, “Data Integration in Presence of Authorization Policies,” in 2012 IEEE 11th International Conference on Trust, Security and Privacy in Computing and Communications, 2012, pp. 92–99. • S. W. Van Den Braak, S. Choenni, R. Meijer, and A. Zuiderwijk, “Trusted third parties for secure and privacy-preserving data integration and sharing in the public sector,” in Proceedings of the 13th Annual International Conference on Digital Government Research - dg.o ’12, 2012, pp. 135 –144. • A. Morton and M. Sasse, “Privacy is a process, not a PET: a theory for effective privacy practice,” in Proceedings of the 2012 workshop on New security paradigms, 2012, pp. 87–104. • S. S. Bhowmick, L. Gruenwald, M. Iwaihara, and S. Chatvichienchai, “PRIVATE-IYE: A Framework for Privacy Preserving Data Integration,” in 22nd International Conference on Data Engineering Workshops (ICDEW’06), 2006, pp. 91–91.

  9. Questions & Comments?

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