Abstract: A system, computer program product, and method is described to propose a method to automatically suggest a visualization providing the best insight for a given dataset, by maximizing an utility function computed on properties of the input data, and previously used visualizations by the same user and for the same data. In one example method for recommending a data visualization in a computer system includes receiving at least one input dataset and at least one set of previous visualizations. A utility function is accessed. A set of potential visualizations is computed using the input dataset and the set of previous visualizations. A recommendation is selected for a proposed visualization from the set of potential visualizations by maximizing a value of the utility function, whereby the proposed visualization is a new visualization not in the set of previous visualizations.

Abstract: A method, system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A method, system and computer program product for preventing sensitive pattern disclosures from Linked Data graphs. The proposed method (i) receives as input a Linked Data graph and a set of query patterns that correspond to sensitive knowledge that needs to be concealed, and (b) minimally distorts the Linked Data graph to generate a sanitized counterpart (graph) in which only the non-sensitive patterns can be discovered. The method supports a variety of utility functions, which it optimizes during the graph sanitization process. The resulting, sanitized graph can be subsequently used for publishing and/or querying purposes.

Abstract: A method, system and computer program product for preventing sensitive pattern disclosures from Linked Data graphs. The proposed method (i) receives as input a Linked Data graph and a set of query patterns that correspond to sensitive knowledge that needs to be concealed, and (b) minimally distorts the Linked Data graph to generate a sanitized counterpart (graph) in which only the non-sensitive patterns can be discovered. The method supports a variety of utility functions, which it optimizes during the graph sanitization process. The resulting, sanitized graph can be subsequently used for publishing and/or querying purposes.

Abstract: A method, system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: A system and computer program product for transforming a Linked Data graph into a corresponding anonymous Linked Data graph, in which semantics is preserved and links can be followed to expand the anonymous graph up to r times without breaching anonymity (i.e., anonymity under r-dereferenceability). Anonymizing a Linked Data graph under r-dereferenceability provides privacy guarantees of k-anonymity or l-diversity variants, while taking into account and preserving the rich semantics of the graph.

Abstract: Presented is an electronic data sharing agreement authoring system and method for creating an electronic sharing agreement comprising at least one statement for defining data sharing between entities and conforming to a predefined syntax.