Towards search on encrypted graph data

We present an approach where one can execute user defined SPARQL queries on encrypted graph data. The graph data is only partially revealed to those users authorised for executing a query. The approach is based on eight different types of queries, corresponding to the different binding possibilities in a single SPARQL triple pattern. The allowed queries can be further restricted by the owner of the graph data, e. g., through pre-defining a specific predicate or object. Single triple patterns can be combined to query group patterns as they can be stated in SPARQL queries and allow to execute a wide range of SELECT and ASK queries

OPTIMIZATION OF NONSTANDARD REASONING SERVICES

The increasing adoption of semantic technologies and the corresponding increasing complexity of application requirements are motivating extensions to the standard reasoning paradigms and services supported by such technologies. This thesis focuses on two of such extensions: nonmonotonic reasoning and inference-proof access control. Expressing knowledge via general rules that admit exceptions is an approach that has been commonly adopted for centuries in areas such as law and science, and more recently in object-oriented programming and computer security. The experiences in developing complex biomedical knowledge bases reported in the literature show that a direct support to defeasible properties and exceptions would be of great help. On the other hand, there is ample evidence of the need for knowledge confidentiality measures. Ontology languages and Linked Open Data are increasingly being used to encode the private knowledge of companies and public organizations. Semantic Web techniques facilitate merging different sources of knowledge and extract implicit information, thereby putting at risk security and the privacy of individuals. But the same reasoning capabilities can be exploited to protect the confidentiality of knowledge. Both nonmonotonic inference and secure knowledge base access rely on nonstandard reasoning procedures. The design and realization of these algorithms in a scalable way (appropriate to the ever-increasing size of ontologies and knowledge bases) is carried out by means of a diversified range of optimization techniques such as appropriate module extraction and incremental reasoning. Extensive experimental evaluation shows the efficiency of the developed optimization techniques: (i) for the first time performance compatible with real-time reasoning is obtained for large nonmonotonic ontologies, while (ii) the secure ontology access control proves to be already compatible with practical use in the e-health application scenario.

Query-based access control for ontologies

Abstract. Role-based access control is a standard mechanism in information systems. Based on the role a user has, certain information is kept from the user even if requested. For ontologies representing knowledge, deciding what can be told to a user without revealing secrets is more difficult as the user might be able to infer secret knowledge using logical reasoning. In this paper, we present two approaches to solving this problem: query rewriting vs. axiom filtering, and show that while both approaches prevent the unveiling of secret knowledge, axiom fil-tering is more complete in the sense that it does not suppress knowledge the user is allowed to see while this happens frequently in query rewriting. Axiom filtering requires that each axiom carries a label representing its access level. We present methods to find an optimal axiom labeling to enforce query-based access restric-tions and report experiments on real world data showing that a significant number of results are retained using the axiom filtering method.