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This paper makes seven contributions to security aggregation research. It identifies inference aggregation and cardinality aggregation as two distinct aspects of the aggregation problem. The paper develops the concept of a semantic relationship graph to describe the relationships between data and then presents inference aggregation as the problem of finding alternative paths between vertices on the graph. An algorithm is presented for processing the semantic relationship graph to discover whether potential inference aggregation problems exist. A method of detecting some aggregation conditions within the DBMS is presented which uses the normal DBMS query language and adds additional catalytic data to the DBMS to permit a query to make the inference. The paper also suggests use of set theory to describe aggregation conditions and the addition of set operations to the DBMS to permit the description of aggregation detection queries.

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This paper describes the development of a data mining system that is lo operate on NASA? Information Power Grid (IPG). Mining agents will be staged to one or more processors on the IPG. There they will grow using just-intime acquision of new operations. They will mine data delivered using just-in-time delivery. Some initial experimental results are presented.

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This paper presents a second-path inference-detection approach based on association cardinalities.* It is applicable to the detection of second paths that do not involve functional dependencies or foreign keys. It provides for an analysis sieve that begins with the analysis of an object model of the database. The goal of the analysis is to detect cases in the database in which a small number of values in the target entity can be associated with a single value in the anchor entity. The number of values is called the association cardinality from anchor to target. Inference vulnerabilities occur for cases of small association cardinalities. The analysis sieve processes the data model of the database to detect cases of small association cardinality. For cases with high cardinality associations, the sieve mines the database to detect cases of small instance-level association cardinalities.