Fusing restricted information

TL;DR

This paper proposes a method for data fusion that maintains security restrictions, enabling the sharing of useful information without revealing sensitive details, thus improving situational awareness.

Contribution

It introduces a novel approach to fuse data with security classifications, producing classifiers that prevent sensitive information disclosure while preserving utility.

Findings

Proposed a classifier that removes sensitive information.

Ensures adversaries cannot infer classified data.

Enhances information sharing without compromising security.

Abstract

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.