Ricerc@Sapienza

Semantic technologies combine knowledge representation and artificial intelligence techniques in order to achieve a more effective management of enterprise knowledge and data bases. In this context, Ontology-based Data Management (OBDM) has consolidated itself as a paradigm for integrating, sharing and governing data, based on a three-tier architecture, in which an ontology, i.e., a conceptual formalization of the business domain, is connected to autonomous data sources through declarative mappings.

In the presence of sensitive information, data access needs to be properly regulated. However, state-of-the-art OBDM techniques and systems do not provide any support to the protection of confidential data, even though they proved themselves to be perfectly suited for data sharing and distribution.

In this project we aim at filling this gap, and at developing methods and tools for data privacy and security in OBDM. To this aim we will revisit and adapt to OBDM the Controlled Query Evaluation (CQE) framework, in which confidential data are protected through a policy specifying the information that cannot be disclosed and (optimal) censors (minimally) alters answers to user queries in order to preserve the secrets. By virtue of the declarative, logic-based nature of both frameworks, we believe that their marriage is natural and effective. At the same time, it is also really challenging, since CQE has been so far mainly studied in the context of databases, and very few works have instead considered it in the presence of ontologies. Thus, a clear, systematic view of the CQE problem over ontologies, and a fortiori in OBDM, is still missing to date. Thus, our specific objectives will be: studying fundamental research issues and developing effective algorithms for CQE over ontologies and in OBDM; implementing these algorithms in tools; testing them on real-world use cases characterized by the presence of highly sensitive information.