You are here

Automated assessment of disclosure risk

Award Information
Agency: Department of Defense
Branch: Air Force
Contract: FA8750-14-C-0042
Agency Tracking Number: F13A-T14-0100
Amount: $149,999.00
Phase: Phase I
Program: STTR
Solicitation Topic Code: AF13-AT14
Solicitation Number: 2013.A
Solicitation Year: 2013
Award Year: 2014
Award Start Date (Proposal Award Date): 2013-10-23
Award End Date (Contract End Date): 2014-07-22
Small Business Information
1050 W NASA Blvd Suite 155
Melbourne, FL -
United States
DUNS: 038379579
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Bruce McQueary
 Vice President, R&D
 (321) 591-7371
Business Contact
 Lynn Lehman
Title: CEO
Phone: (919) 244-3946
Research Institution
 Dartmouth College
 Shea McGovern
Office of Sponsored Projects 11 rope Ferry Road #6220
Hanover, NH 03755-1404
United States

 (603) 646-3007
 Nonprofit College or University

ABSTRACT: Information systems continue to progress in terms of collecting, characterizing and assessing information. While this evolution has provided unprecedented intelligence capability to the U.S. and our allies, it has also raised unique challenges in the area of information security and disclosure risks. In particular, the intelligence community (IC) currently lacks the ability to understand how the continuous release of information through approved information sharing, intentional or unintentional leaks, or malicious, covert breaches risks divulging"secrets"either directly or through inference. To address this gap Securboration Inc. proposes an innovative approach that combines and extends Securboration"s semantics-based text analytic summarization pipeline with machine learning and probabilistic reasoning. Our solution, referred to as RIQUEST (Risk Quantification and Estimation Toolkit), will compute the disclosure risk for a given piece of information with respect to a large number of secrets. RIQUEST includes a robust ontology-based model of disclosure risk categories, and quantifies risks within subsets of those categories. RIQUEST addresses the problem of disclosure through probabilistic inference, and takes into account the contextual information that enables inference. BENEFIT: The commercial potential for RIQUEST is significant. Big-four auditing firms are aggressively pursuing data loss prevention (DLP) as a business line for their established customers. The process of identifying an organization"s sensitive data and understanding the risk of its exposure is a critical first step in any DLP business model. RIQUEST"s ability to automate this process represents a significant improvement over the manual data classification exercises currently employed. Whether in commercial enterprises or the Air Operations Center, RIQUEST will provide cumulative, continuous assessment resulting in greater disclosure risk situational awareness which, in turn, leads to improved information sharing and the ability to focus security countermeasures in response to leaks.

* Information listed above is at the time of submission. *

US Flag An Official Website of the United States Government