Fusing restricted information
This addresses a domain-specific problem for organizations needing to share fusion results without compromising security, but it appears incremental as it builds on existing fusion and data mining processes.
The paper tackles the problem of information fusion when inputs have security classifications, which restricts dissemination and reduces situational awareness. It proposes a method to produce a classifier with sensitive information removed, ensuring that classified data cannot be inferred from the output.
Information fusion deals with the integration and merging of data and information from multiple (heterogeneous) sources. In many cases, the information that needs to be fused has security classification. The result of the fusion process is then by necessity restricted with the strictest information security classification of the inputs. This has severe drawbacks and limits the possible dissemination of the fusion results. It leads to decreased situational awareness: the organization knows information that would enable a better situation picture, but since parts of the information is restricted, it is not possible to distribute the most correct situational information. In this paper, we take steps towards defining fusion and data mining processes that can be used even when all the underlying data that was used cannot be disseminated. The method we propose here could be used to produce a classifier where all the sensitive information has been removed and where it can be shown that an antagonist cannot even in principle obtain knowledge about the classified information by using the classifier or situation picture.