Computational Policies in a Need to Share EnvironmentComputational Policies in a Need to Share Environment

Tim Finin

University of Maryland,Baltimore County

SemGrail workshop, Redmond WA, 21 June 2007

Introduction

• Comments on the role of and require-ments for computational policies in today’s environment– Web, 9/11, pervasive computing, …

• Ideas in development in collaboration with colleagues from UT Dallas, GMU, and MIT

Background

• We’ve been working on computational policies since about 1999 – Started with XML encoded horn clauses for supply

chain mgmt & pervasive computing

• Moved policy research to the Semantic Web in ~2002 – Lalana Kagal developed Rei in her dissertation

• Applications have varied– Also enhanced P3P, service matching and

selection, collaborative teams, RDF store access, and distributed router configuration.

Policy-based Automated Wide-Area Network Configuration and Management

PbA -WAN-C&M 001 v1

Management – configuration, planning, and control

Planning agents emulate operational environment to create and test configuration policies

Operation – deployment and control

Embedded agents verify, validate, and enforce local and global configuration

Configuration Policy Embedded agents share and

diagnose local and remote states to detect and respond

to aberrant behavior

Planning agents collect information from operational network to provide emulation and analysis feedback

Role -based PKI control access for restricting

capabilities of network administrator

Cross -system secure messaging and policy-based PKI authentication and authorization control

OWL + SWRL based ontologies and rules to define advanced configuration policies

Goal: self configuring network routers running in a coalition envi-ronment demonstrating constraints on border gateway protocol

General approach

• A computational policy describes a system’s actions or behavior

• “Describes” can be– Specifies: whenever X, do Y– Constrains: doing X is permitted– Advises: whenever X, doing Y is preferred

to doing Z

• Public policies and common policies foster interoperability and cooperation

Some lessons learned

• Most of the work in developing a policy is in developing the domain ontology– Often the constraints are simple, e.g., “For

faculty use only”

• Sharing policies means sharing domain models– The Semantic Web offers a sound and

practical approach for shared domain models

Some lessons learned• Several approaches to encoded the rules or

constraints part of policies– Descriptions of permitted, forbidden and obliged

classes of actions (KAOS)– Using rule extensions to RDF (Rei, Rein)

• Some approaches are problematic– E.g., uncertainty, probabilities, defaults

• But OWL can do the heavy lifting in reasoning about the terms– Is Mary a full-time faculty member from a higher-

educational institution? What’s the evidence?

New Requirements

• 9/11 and related events illustrated problems in how sensitive information is managed

• Managing information and services on the Web with appropriate security and privacy and simplicity is increasingly important and challenging

• Autonomous devices like mobile phones, routers and medical equipment need access too.

Need to Know, Need to Share

• Traditional information security frame-works are based on “need to know”

Unless you can prove that you have a pre-arranged right to this information, you can’t have it

• The 9/11 commission recommended moving from this to “need to share”

I think this information may be important for you to accomplish your mission and would like to share it with you

Need to Know, Need to Share

• Traditional information security frame-works are based on “need to know”

Unless you can prove that you have a pre-arranged right to access this information, you can’t have it

• The 9/11 commission recommended moving from this to “need to share”

I think this information may be important for you to accomplish your mission and would like to share it with you

Just a slogan?

• For “need to share” to be more than just a political slogan, we need to under-stand what it might mean technically

• … and to explore its feasibility and desirability

• … and the risks and benefits

Required Capabilities

• Semantic Interoperability• Unknown principals• Context• Speech acts and negotiation• Adjustable privacy• Usage control, enforcement,

accountability• Explanations and provenance• Ramifications

Semantic Interoperability

• Having a shared policy requires that the parties agree on– The semantics of the policy language (e.g.,

is everything not explicitly forbidden allowed?)

– The semantics of the domain ontology (e.g., who’s a faculty member?)

• The Semantic Web is a big win here.

Unknown Principles

• Standard access control is based on authentication– I have a list of who can do what. Just

prove to me which of these people you are

• In open environments (Web, pervasive computing) this won’t work

• We can control access based on their their (provable) attributes– Prove you’re a current UMBC student to

use the printer

Context

• What’s forbidden in a normal situation may be allowed in a life-threatening emergency

• Context descriptions (e.g., tags) can identify the current situation

• Policy rules can be conditioned by context– E.g., as guards on rules or by enabling/

disabling policy modules

Adjustable privacy

• One way to enforce privacy is to not divulge information

• Another is to provide general answers• Where’s John?

– [47.670412403362256, -122.12013959884644]

– In Redmond– In Washington state– On travel

• Policies can control the granularity of answers given to different queries

Usage control and accountability

• Enforcing policies can be a difficult issue in open, distributed systems

• MIT’s policy aware approach is exploring accountability for use– Policy violations can be detected in logs

• There’s lots more to usage constraints– E.g., DRM policies constrain how often you

can perform certain operations on an object

• Systems need to reason about there own behavior as well as that of others

Explanations and provenance

• Explaining why a policy decision holds or doesn’t hold can be important– Explaining why a constraint does not hold

continues to be a difficult task

• The explanation may involve provenance, citing the source for the facts and policy constraints used

Utility and Ramifications

• In some environments, the utility of data may be a factor in whether to share or not– This requires reasoning about the requestor’s

tasks, the data’s relevance to them and the availability of alternate data

• This may also require Bayesian reasoning– What’s the likelihood that the patient might have

diabetes?

• In general, a system might reason about the risks and benefits of sharing vs.. not sharing the data

Planned Architecture

PolicyEngine

OWLReasoner

UtilityReasoner

PolicyOntology

UtilOnt

BayesOnt

Domain Ontology

OWL

Policy Rules Instance Data

RDF

SP

AR

QL

Conclusion

• Managing information in open, distributed environments with appropriate security and privacy is increasingly important

• Computational policies can help• Semantic Web technologies offer a way to

share common policy concepts, policies, and domain models

• Other representation and reasoning compo-nents will be needed for many application domains.

http://ebiquity.umbc.edu/