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Design for Addressing Data Privacy Issues in Legacy Enterprise Application IntegrationMeddeoda Gedara, Kavindra Kulathilake January 2019 (has links)
Electronic message transfer is the key element in enterprise application integration (EAI) and the privacy of data transferred must be protected by the systems involved in the message transfer from origin to the destination. The recent data privacy regulation such as GDPR (General Data Protection Regulation) has enforced the organizations to ensure the privacy of the personal data handled with obligations to provide visibility and control over to the data owner. Privacy concerns with relevant to sensitive data embedded and transferred through business-to-business (B2B) middleware platforms in enterprise architecture are mostly at risk with the legacy nature of the products and the complexity of system integrations. This poses a great threat and challenge to organizations processing sensitive data over the interconnected systems in complying with regulatory requirements. This research proposes a solution design to address the data privacy issues related to personal data handled in an enterprise application integration framework. Where electronic messages used to transfer personally identifiable information (PII). The proposal consisting of a design called “Safety Locker” to issue unique tokens related to encrypted PII elements stored in a persistence data storage based on Apache Ignite. While adding REST API interfaces to access the application functionality such as tokenization, de-tokenization, token management and accessing audit logs. The safety locker can run as a standalone application allowing clients to access its functionality remotely utilizing hypertext transfer protocol (HTTP). The design allows the data controllers to ensure the privacy of PII by embedding tokens generated from the application within the electronic messages transferred through interconnected systems. The solution design is evaluated through a proof of concept implementation, which can be adapted, enhanced to apply in EAI implementations.
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