A Policy-Driven Information Exchange Network 

by Mark R. Mittrick, John T. Richardson, and Richard C. Kaste 

ARL-MR-704 July 2008 

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Army Research Laboratory 

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ARL-MR-704 July 2008 

A Policy-Driven Information Exchange Network 

Mark R. Mittrick, John T. Richardson, and Richard C. Kaste 

Computational and Information Sciences Directorate, ARL 

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A Policy-Driven Information Exchange Network 

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W911NF-05-2-0039 

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6. AUTHOR(S) 

Mark R. Mittrick, John T. Richardson, and Richard C. Kaste 

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14. ABSTRACT 

Tactical information exchange, involving levels of interdependent and sometimes conflicting policies, presents many 

difficulties. This is a critical problem for today’s fighting forces, one that the U.S. Army Research Laboratory’s Brigade and

Below Battlespace Awareness Network project is addressing. 

In certain battlefield situations, mechanisms must be implemented to allow information to move across domains. A major 

objective of the project is to develop decision-making capabilities for releasing information, using KAoS, which was developed 

at the Institute for Human and Machine Cognition. KAoS policy services allow for the specification, management, conflict 

resolution, and enforcement of policies within domains. In an attempt to remedy the cross-domain issue, these services test 

policies that incorporate tactical awareness into the information release decision. 

Innovative solutions require changes in information policy as well as new models and methods. Requirements for and 

impediments to tactical information exchange, integration with academic/industrial software, simulated data sets for 

experimentation, and techniques for implementing a prototype are investigated. This report provides an overview of our 

research in support of a policy-driven information exchange network. 

15. SUBJECT TERMS

B3AN, policy information exchange, KAoS redaction 

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Mark Mittrick 

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ii
Contents 

List of Figures iv

Acknowledgments v

1. Introduction 1

2. Challenges 1

3. Scenario 3

4. Policies 4

4.1 Authorization Policies.....................................................................................................4 

4.2 Approval Policies............................................................................................................4 

4.3 Redaction Policies...........................................................................................................5 

4.4 Notification Policies........................................................................................................5 

5. Policy Aware Information Exchange 5

6. Possible Research 7

7. Conclusion 9

Appendix. Redaction Code 11 

Distribution List 15 

iii
List of Figures 

Figure 1. McKenna (Fort Benning, GA) MOUT............................................................................4 

Figure 2. BCT client. ......................................................................................................................6 

Figure 3. AttackMission ontology snippet......................................................................................6 

Figure 4. SITREP meta-data...........................................................................................................6 

Figure 5. Redaction meta-data. .......................................................................................................7 

iv
Acknowledgments 

This report would not have been possible without the outstanding efforts of Eric Heilman, Larry 

Bunch, and Jeff Bradshaw. Mr. Heilman was instrumental in developing the scenario and 

associated data products. Mr. Bunch was the KAoS technical lead and provided much auxiliary 

programmatic support. Dr. Bradshaw interfaced with the California State University San 

Bernardino Policy Working Group and offered valuable suggestions. This work was performed 

under the auspices of two U.S. Army Research Laboratory Collaborative Agreements (W911NF05-2-0039, Foundation for CSU San Bernardino; W911NF-07-2-0088, Policy Governed Data 

Fusion). 

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vi
1. Introduction 

The goal of the U.S. Army Research Laboratory’s (ARL’s) Brigade and Below Battlespace 

Awareness Network (B3AN) project is the realization of a tactical information exchange system 

drawing upon various intelligence sources available to the military. An important aspect of the 

project is the handling of heterogeneous data types and sources. For example, data could include 

unmanned aerial vehicle imagery or a “spot report” in audio or text format. Furthermore, rapid 

exchange of available information is essential for success in urban conflicts. Accurate and 

timely intelligence is critical to maintaining the situational awareness needed for battlefield 

dominance. Despite the need for rapid exchange, it is critical for any solution to account for 

existing policies governing access to these data types and sources in order to remain consistent 

with security protocols. The technical challenge of B3AN is designing an automated information 

exchange system while attempting to maintain security requirements. 

One product from this effort is a software system to demonstrate the utility of a policy engine. 

Developing and testing such software entails pursuing and applying of scholarly research with 

our academic partners concerning requirements for and impediments to cross-domain 

information exchange, prototypical components through extensions to semantic 

filtering/transformations, and possible integration with academic/industrial software. 

The work here is qualitatively different from and complementary to other related efforts pursued 

at ARL. This was a new beginning; because a sensitive area and somewhat controversial 

approaches are involved, the technical and programmatic aspects are complicated. This work 

does not intend to short-circuit existing security policy procedures or guard development and 

implementation mechanisms; rather, the tactical application is considered a research thrust. 

2. Challenges 

A formulation of the overall problem might be how a “classified” data item can be transformed 

for release to a “lesser-cleared” user to maximize the value of perishable information. The 

hypothesis is that bringing “classified” information, with policy issue resolution, to a lesserclassified level is a powerful enabler of situation understanding. A more general problem would 

involve maximizing the value of released information relative to arbitrary security criteria. 

The system needs to define “need to know,” which can take a variety of forms and include 

temporal, geographic, or organizational aspects. A temporal aspect to need to know could be 

based on relevance of the information to the user. The Soldier needs to know what is around the 

corner—the information is relevant to the Soldier only at that moment. To an analyst in the 

1
2

chain of command, the information could be more relevant with regard to battle damage 

assessment or predictive course of action development. Those involved in training might also 

use the information for a very long time after the event. 

When a unit is performing a mission, it is bounded by an area of operations (AO), or area of 

responsibility, to ensure mission execution without interfering with another unit. Geographic 

qualification would include the Soldier’s relation to the event being viewed. The relationship 

could be defined by proximity or by the fact that the Soldier and event are in the same AO. 

Similarly, the notion of chain of command must be considered as a factor in tactical information 

access by the Soldier. The system must address the need to know of the chain and associated 

analysts. That is, for most U.S. Army situations, the Soldier’s actions are ordered or coordinated 

by other personnel involved with the mission. These people must generally be permitted to 

monitor inputs to the operator. Need to know is also complicated by the existence of combat 

support and combat service support entities. For example, artillery may not be in the direct chain 

of command but could be involved in aspects of the specific battlespace, requiring intelligence 

concerning portions of the execution. This could be associated not only with positive support but 

also with mishap avoidance. 

One component of the research involves rationales for requiring certain pieces of information. 

This leads to philosophical considerations that combine tactical aspects of the scenario and larger 

implications of transformation and dissemination. For instance, if one knew he or she were 

walking into an ambush, a certain sensing would help. If the system has such information, 

should it be passed along in any event? What if passing it along might cause greater damage 

than the benefit? One could argue that a central clearinghouse is needed; in some schemes, the 

human security officer fills this role. It often appears that many real-world problems do not lend 

themselves to automatic policy processing and that amenable situations are contrived and 

simplistic. This situation can at least be partially addressed by today’s information technology. 

A hybrid approach that utilizes emerging semantic web technology seems appropriate in the fastpaced complex battlespace. 

The notion of situational imperative is essential to policy development for tactical information 

release. This entails assessing the necessity of information to the recipient as well as of 

associated urgency. (Note that necessity and urgency may be related but are separate concepts.) 

A fact may be necessary to avoid a tactical error, in general, but it is not urgent. An example 

might be the size of the enemy force in the next sector. Similarly, the notion of information 

perishability is required. In general, assessing value of information is a significant research 

problem; assessing time- or event-dependent value, required for tactical policy solutions, is even 

more complicated. 
The total policy system should take into account time intervals and scheduling with regard to 

transformation, release, and use of information. Another related complication involves the 

notions of partial mission completion and partial information release. It is tempting to think of a 

“complete” fact or sensing as impacting an “entire” mission. However, there is a spectrum of 

mission completion, in terms of goals accomplished and progress toward accomplishment. The 

developers and users of policy systems must take into account that information may be released 

piecemeal to subsets of the agents working a mission. The reason information may be released 

piecemeal is that the calculations of situational imperative will likely assign varying 

prioritizations. 

3. Scenario 

One of the goals here is to construct a demonstration that will help convince policy makers of the 

need to change current information-sharing methods. In this demonstration, an event (e.g., a new 

threat report) will precipitate a proper mission response and illustrate how emerging technology 

might ameliorate situations in which existing policy/procedures would have interfered with 

situational awareness and mission execution. The following is the sequence from this effort: 

• The 3rd brigade combat team (BCT) is on a peace-keeping mission in central and southern 

Diyala. 

• Delta Company (part of 3rd BCT) is located in southern Diyala. 

• Alpha Company (part of 3rd BCT) is located in northern Diyala. 

• Alpha Company collects human intelligence (HUMINT) report concerning an incendiary 

explosive device (IED) factory in a small town. 

• The IED factory may relocate at anytime, so the HUMINT is perishable. 

• The division orders 3rd BCT to investigate the IED factory. 

• The 3rd BCT creates a fragmentary order (FRAGO) for Delta Company to perform cordon 

and search on the village. 

• B3AN helps determine the intelligence package that Delta Company gets for its mission. 

• Delta Company destroys the IED factory and captures high-profile terrorists and 

documents. 

For visualization, existing McKenna military operations in urban terrain (MOUT) site 

backgrounds and tactical overlays were used (figure 1). 

3
Figure 1. McKenna (Fort Benning, GA) MOUT. 

4. Policies 

The policies that the team derived for the scenario are listed next and can be found in more detail 

in the paper “Policy-Governed Information Exchange in a U.S. Army Operational Scenario.”1

4.1 Authorization Policies 

• Brigade Combat Team (BCT) members are authorized to access documents that have a 

classification level of secret or below (e.g., secret, sensitive, and unclassified). 

• Company members are authorized to access documents that have a classification level of 

secret and are perishable. 

• Company members are authorized to access documents that have a classification level of 

sensitive or below. 

4.2 Approval Policies 

• BCT members are obligated to approve Company member access to documents that are 

sensitive or above. 

 1

Institute of Human and Machine Cognition. Policy-Governed Information Exchange in a U.S. Army Operational Scenario. 

The 2008 IEEE Workshop on Policies for Distributed Systems and Networks, Palisades, NY, June 2008. 

4
• Except: BCT members are not obligated to approve company member access to HTML 

documents that are sensitive and perishable. 

4.3 Redaction Policies 

• BCT members are obligated to redact source-identifying text for Company member 

accesses to HTML documents that are sensitive or above. 

4.4 Notification Policies 

• BCT members are obligated to notify the creator of any orders containing priority INTEL 

requirements that match the features of a received document. 

5. Policy Aware Information Exchange 

ARL, in collaboration with the Institute for Human Machine Cognition (IHMC), developed a 

demonstration system to test an automated policy-based solution to tactical information 

exchange inside the bounds of the scenario. The system uses IHMC’s KAoS policy services 

framework to determine the intelligence information available for a mission. KAoS employs the 

Web Ontology Language (OWL) to represent and reason about the policies defining access to 

intelligence documents. The system requires an XHTML representation of the intelligence 

documents to enable the embedding of ontology information in the form of resource description 

framework (RDF) attribute markup. Finally, the system uses Simple Protocol and RDF Query 

Language (SPARQL), a language that can perform queries on OWL, to identify documents 

relevant to the mission and filter the results based on policies represented in KAoS. 

To illustrate the process of matching appropriate intelligence documents to a mission, consider 

the situation displayed in figure 2. This figure represents the point in the scenario when the 3rd 

BCT has issued a FRAGO to Delta Company. The BCT intelligence analyst has loaded an 

XHTML version of the FRAGO into the demonstration client, enabling the system to parse the 

mission type (AttackMission) and AO (village T-12). The SPARQL query coded into the system 

will identify intelligence documents relevant to an attack mission in the given AO. The ontology 

defines relevant information for attack missions (figure 3). 

It is evident why the system identified the document highlighted in figure 2 (0831 SITREP) as 

relevant. The RDF meta-data embedded in this report (figure 4) reveals that the document 

includes information in the target AO (village T-12). Furthermore, the report represents an 

enemy threat (no. a_EnemyThreat in the meta-data), which matches a type of relevant 

information for an AttackMission displayed in figure 3 (no. a_Threat). 

5
Figure 2. BCT client. 

Figure 3. AttackMission ontology snippet. 

Figure 4. SITREP meta-data. 

6
Next, the SPARQL query filters the identified documents against the authorization policies to 

determine release eligibility. The authorization policies defined in KAoS state that the BCT is 

allowed to access the document (0831 SITREP) because its embedded meta-data defines it as 

secret and perishable (figure 4). Furthermore, the policies obligate the BCT commander to 

approve or disapprove the release of the report to Delta Company. According to policy, if a 

document represents information above sensitive classification, then commander approval is 

required before releasing it for mission use. This security control is in the BCT client (figure 2) 

as a check box next to the document. Finally, if the document is used for the mission, then any 

statements identifying the source (i.e., informant name) of the intelligence must be redacted. 

This condition stems from another policy concerning documents classified above sensitive. 

Redaction is possible because the intelligence documents are written in XHTML. This format 

allowed ARL developers to use an extensible markup language (XML) parser to find and remove 

elements containing source-revealing information by searching for relevant RDF attribute 

markup. For example, in figure 5, the highlighted code includes RDF attribute markup 

identifying the text ‘P-3’ identifying the source of the imagery. In the mission version of this 

document, the text ‘P-3’ will not be included. The source code for the redaction methods is 

available in the appendix. 

Figure 5. Redaction meta-data. 

6. Possible Research 

Our initial investigations have precipitated several research possibilities. As the software 

implementations of the foundational policy work are developed, approaches to and implications 

of related problems are considered. Working on these will lead to improvements in the B3AN 

7
model and better approaches to tactical policy semantics. These vary considerably, and overlap 

somewhat, but can be grouped roughly into a few categories. 

One category deals with fundamental issues of information in general. For instance, can 

appropriate mathematics dealing with measurement or calculation of the value of information be 

developed? How about the mathematics of risk? The problem involves several preliminary 

semantic, almost philosophical aspects. Certain “parameters” must first be defined before ways 

to determine or measure their values can be discussed. 

Another category is explicitly tactical and applied. Here, techniques for measuring or calculating 

perishability, urgency of mission, and proximity in space/time/mission are considered. These 

things may require subject matter expert consultation, particularly with regard to partial mission 

completion. Tactical classification is not (totally) ordered; it is more akin to “need to know.” 

Perhaps the following new “security categories” other than (U) and (S) should be recognized: 

types (A) and (B), depending on the situational parameters discussed. Is it possible for any item 

to be transformed? If not, what are the policy repercussions? Is it possible for this whole 

process to be completely automated by computers such as the fictional SkyNet? Can there be 

notions of the “inherent” classification of composite/transformed data? 

How can cost (in terms of time, fidelity, and robustness) of the process, particularly with a 

human in the loop, be properly accounted for? 

A kind of hybrid category between theoretical and applied involves questions listed next. How 

can a security policy model handle recipient knowledge of the existence of data fields? How 

about assessing the ability of an entity to deduce unauthorized information? These things are 

related to the practical notion of constraining types of queries in order to mitigate unintended 

disclosure. What are the implications of partial release (e.g., can release these parts to these 

entities), even if explicit deduction does not apply? 

Continuing with hybrid aspects, it is difficult to evaluate the downside of inappropriate release. 

For instance, although information may be vital to mission completion, there is a finite chance 

that it may fall into the hands of the enemy. Items and subitems entering into policy applications 

are typically not independent. Many interrelated and even essentially unquantifiable aspects 

could make it an impossible problem, or these aspects could seem so situation-dependent that 

human decision makers must be involved. 

A thesis to prove or disprove the further research just outlined would extend the basic hypothesis 

as set forth in this report. This extended hypothesis, which can be dealt with analytically, can be 

stated as follows: situational imperative and information perishability is necessary but not 

sufficient. 

8
7. Conclusion 

A policy-driven information exchange network would ideally have many benefits, including 

secure automation of configuration, quality of service, and other aspects of network 

management. It is hoped that groundbreaking work on rule-based access control by IHMC will 

lead to improvements in extensibility, verifiability, and efficiency. Planning for longer term 

extensions includes studying scholarly research; aligning tasks with this research; integrating 

with academic/industrial software; enabling web services aspects (e.g., agents); refining rules; 

and developing a robust object-based B3AN policy model. 

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10
Appendix. Redaction Code 

 

This appendix appears in its original form, without editorial change. 

11
APPENDIX 

Redaction Code 

package b3an.redact; 

import java.io.ByteArrayOutputStream; 

import java.io.InputStream; 

import java.io.OutputStream; 

import java.util.Vector; 

import org.apache.xerces.parsers.DOMParser; 

import org.w3c.dom.DOMImplementation; 

import org.w3c.dom.Document; 

import org.w3c.dom.Node; 

import org.w3c.dom.bootstrap.DOMImplementationRegistry; 

import org.w3c.dom.ls.DOMImplementationLS; 

import org.w3c.dom.ls.LSOutput; 

import org.w3c.dom.ls.LSSerializer; 

import org.w3c.dom.traversal.DocumentTraversal; 

import org.w3c.dom.traversal.NodeFilter; 

import org.w3c.dom.traversal.NodeIterator; 

import org.xml.sax.InputSource; 

public class B3AN_Redact implements TextTransform { 

 public B3AN_Redact() { 

 } 

 public String transform(Vector<String> strings, InputStream in) 

 throws Exception { 

 DOMParser p = new DOMParser(); 

 p.parse(new InputSource(in)); 

 Document d = p.getDocument(); 

 DocumentTraversal traversal = (DocumentTraversal) d; 

 NodeIterator nIterator = traversal.createNodeIterator(d 

 .getDocumentElement(), NodeFilter.SHOW_ALL, null, true); 

 Node n = nIterator.getRoot(); 

 while (n != null) { 

 boolean remove = false; 

 if (n.hasAttributes()) { 

 for (int i = 0; i < n.getAttributes().getLength(); i++) { 

 for (int j = 0; j < strings.size(); j++) 

 if (n.getAttributes().item(i).getTextContent() 

 .compareTo(strings.elementAt(j)) == 0) 

 remove = true; 

 } 

 } 

 for (int j = 0; j < strings.size(); j++) 

 if (n.getTextContent().compareTo(strings.elementAt(j)) == 0) { 

 remove = true; 

 } 

 if (remove) 

 n.getParentNode().removeChild(n); 

 n = nIterator.nextNode(); 

 } 

12
13

 System.setProperty(DOMImplementationRegistry.PROPERTY, 

 "org.apache.xerces.dom.DOMImplementationSourceImpl"); 

 DOMImplementationRegistry registry = DOMImplementationRegistry 

 .newInstance(); 

 DOMImplementation domImpl = registry.getDOMImplementation("LS 3.0"); 

 DOMImplementationLS implLS = (DOMImplementationLS) domImpl; 

 LSSerializer dom3Writer = implLS.createLSSerializer(); 

 LSOutput output = implLS.createLSOutput(); 

 OutputStream outputStream = new ByteArrayOutputStream(); 

 output.setByteStream(outputStream); 

 output.setEncoding("UTF-8"); 

 dom3Writer.write(d, output); 

 return outputStream.toString(); 

 } 

}
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