Abstract
Fine-grain access control is now possible to enforce thanks to the adoption of available standard de facto and access control models; they allow to protect data and resources as long as they are properly structured. Furthermore, in many critical domains of the e-government (as the e-health, for example) the introduction of access control mechanisms is needed to respect laws in force on security and privacy. The main problem to face in such contexts is related to the coexistence of both structured and unstructured data; this, indeed, represents a huge limitation for documents management in public and private contexts. In particular, the adoption of unstructured documents, even if stored on digital media, make fine-grain access control mechanisms useless. In this article, we have exploited the adoption of different techniques aiming at analysing texts and automatically extracting relevant information to structure and manage them. We propose a semantic-based framework for data transformation that is able to locate and identify critical sections of medical records to be protected and then enable a protection system to enforce proper security policies.
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