Csilla Farkas

Department of Computer Science and EngineeringUniversity of South Carolina

farkas@cec.sc.edu

Who is Impacted by Cyber Attacks?

Source: http://www.cagle.com/2010/05/internet-privacy/

What is Cyber Security?

Highly Technical

• People, processes, and technology

• Legislation and Regulation

• Risk management

Web Evolution• Past: Human usage – HTTP– Static Web pages (HTML)

• Current: Human and some automated usage – Interactive Web pages– Web Services (WSDL, SOAP, SAML)– Semantic Web (RDF, OWL, RuleML, Web databases)– XML technology (data exchange, data representation)

• Future: Semantic Web Services

ARE THE EXISTING SECURITY MECHANISMS SUFFICIENT TO

PROVIDE DATA AND APPLICATION SECURITY OF THE NEXT GENERATION WEB?

Limitation of Research

• Syntax-based• No association protection• Limited handling of updates • No data or application semantics • No inference control

Secure XML Views - Example

<medicalFiles> UC <countyRec> S <patient> S <name>John Smith </name> UC <phone>111-2222</phone> S </patient> <physician>Jim Dale </physician> UC </countyRec> <milBaseRec> TS <patient> S <name>Harry Green</name> UC <phone>333-4444</phone> S </patient> <physician>Joe White </physician> UC <milTag>MT78</milTag> TS </milBaseRec></medicalFiles>

medicalFiles

countyRec

patient

nameJohn Smith

milBaseRec

physicianJim Dale

physicianJoe White

nameHarry Green

milTagMT78

patient

phone111-2222

phone333-4444

View over UC data

Secure XML Views - Example cont.

<medicalFiles> <countyRec> <patient> <name>John Smith</name> </patient> <physician>Jim Dale</physician> </countyRec> <milBaseRec> <patient> <name>Harry Green</name> </patient> <physician>Joe White</physician> </milBaseRec></medicalFiles>

medicalFiles

countyRec

patient

nameJohn Smith

milBaseRec

physicianJim Dale

physicianJoe White

nameHarry Green

patient

View over UC data

Secure XML Views - Example cont.

medicalFiles

countyRec

patient

nameJohn Smith

milBaseRec

physicianJim Dale

physicianJoe White

nameHarry Green

patient

View over UC data

<medicalFiles> <tag01> <tag02> <name>John Smith</name> </tag02> <physician>Jim Dale</physician> </tag01> <tag03> <tag02> <name>Harry Green</name> </tag02> <physician>Joe White</physician> </tag03></medicalFiles>

Secure XML Views - Example cont.

<medicalFiles> UC <countyRec> S <patient> S <name>John Smith</name> UC </patient> <physician>Jim Dale</physician> UC </countyRec> <milBaseRec> TS <patient> S <name>Harry Green</name> UC </patient> <physician>Joe White</physician> UC </milBaseRec></medicalFiles>

medicalFiles

countyRec

patient

nameJohn Smith

milBaseRec

physicianJim Dale

physicianJoe White

nameHarry Green

patient

View over UC data

Secure XML Views - Example cont.

medicalFiles

nameJohn Smith

physicianJim Dale

physicianJoe White

nameHarry Green

View over UC data

<medicalFiles> <name>John Smith</name> <physician>Jim Dale</physician> <name>Harry Green</name> <physician>Joe White</physician></medicalFiles>

Secure XML Views - Solution

• Multi-Plane DTD Graph (MPG)• Minimal Semantic Conflict Graph

(association preservation)• Cover story• Transformation rules

<medicalFiles>

<milTag>

<phone>

<milBaseRec>

<countyRec>

<patient>

<physician> <name>

TopSecret

Secret

Unclassified

Multi-Plane DTD Graph

D,medicalFiles

D, countyRec D, milBaseRec

D, patient D, milTag

D, name D, phone

UC

UC

UC

S

S

S

TS

TSD, physician

MPG = DTD graphover multiple

security planes

Transformation - Example

name phone

physician

MSCG

MPG

<medicalFiles>

<milTag>

<phone>

<milBaseRec>

<countyRec><patient>

<physician> <name>

TS

UC

S

Security Space Secret

Transformation - Example

MPG

<medicalFiles>

<milTag>

<phone>

<milBaseRec>

<countyRec><patient>

<physician> <name>

TS

S

UC

<emrgRec>

SP

name

physician

MSCG

Transformation - Example

MPG

<medicalFiles>

<milTag>

<phone>

<milBaseRec>

<countyRec><patient>

<physician> <name>

TS

S

UC

<emrgRec>

SPMSCG

Transformation - Example

MPG

<medicalFiles>

<milTag>

<phone>

<milBaseRec>

<countyRec><patient>

<physician> <name>

TS

S

UC SP

<emrgRec>

medicalFiles

emergencyRec

namephysician

Data Structure

The Inference Problem

General Purpose Database:

Non-confidential data + Metadata Undesired Inferences

Semantic Web:

Non-confidential data + Metadata (data and application semantics) + Computational Power + Connectivity

Undesired Inferences

Association Graph• Association similarity measure– Distance of each node from the association root– Difference of the distance of the nodes from the association

root– Complexity of the sub-trees originating at nodes

• Example:

Air show

address fort

XML document:Association Graph:

address fort

Public Public, AC

Correlated Inference

Object[]. waterSource :: Object basin :: waterSource place :: Object district :: place address :: place base :: Object fort :: base

address fortPublic

Water source base

Confidential

district basinPublic

?

Concept Generalization: weighted concepts, concept abstraction level, range of allowed abstractions

21

Correlated Inference (cont.)

address fortPublic

district basinPublic

Object[]. waterSource :: Object basin :: waterSource place :: Object district :: place address :: place base :: Object fort :: base

placebase

Water SourceWater source

Base

Place

Water source base

Confidential

Inference Removal

• Relational databases: limit access to data• Web inferences– Cannot redesign public data outside of protection domain– Cannot modify/refuse answer to already published web

page• Protection Options– Release misleading information– Remove information– Control access to metadata

Big Data Analytics: Are there new questions?

• Technologies• Big Data characteristics–Volume –Variety–Velocity – live database, fast growth

Past: The Inference Problem

Organizational Data

Confidential

Attacker

Public

Access Control

X

OntologyData Integration

andInferences

Web Data

Present: Big Data InferencesPrivate ?

OntologyData Integration

andInferences

Web DataSecure ?

Future: Research Challenges

• Security for raw data– Flexible access control– Data removal

• Security for metadata– Protection need of novel, new concept–Metadata guided attacks

• Cross-context attacks– Correlate data across multiple contexts

Semantic

Web

Technologies

Need for Visualization

Context 1

Context 3

Context 2

Questions?

National Center of Academic Excellence in Information Assurance Education

National Training Standards, Knowledge Units

OUTREACHEDUCATIONRESEARCH

CIAE Mission

CIAE Mission

OUTREACH

EDUCATIONIA courses

IA specializationApplied Computing

Graduate IA Certificate

RESEARCH

K-12 Cyber SecurityEducation

Higher EducationalInstitutes

Industry Partnership

OUTREACHEDUCATION

RESEARCHExternal funding

Peer-reviewed publicationsPh.D. graduates

CIAE Mission