Data and Applications Security Developments and Directions

Data and Applications Security Developments and Directions

Dr. Bhavani Thuraisingham

The University of Texas at Dallas

Lecture #14

Secure Multimedia Data Management and Data Mining

February 24, 2005

Objective

This unit provides an overview of multimedia information management including multimedia data management and multimedia data mining. Security issues will also be discussed

Reference: Managing and Mining Multimedia Databases, CRC Press, Thuraisingham, June 2001

Outline

Multimedia Data Management Systems

- Architecture

- Modeling

- Functions Security Developments and Challenges Multimedia Mining Future Directions

Sources of Multimedia Data

Text, Video, Audio, Imagery

Why Multimedia Database Management System?

Need persistent storage for managing large quantities of multimedia data

A Multimedia DBMS manages multimedia data such as text, images, audio, animation, video

Extended by a Browser to produce a Hypermedia DBMS Heterogeneity with respect to data types Numerous Applications

- Entertainment, Defense and Intelligence, Telecommunications, Finance, Medical

Architectures:Loose Integration

MultimediaFile Manager

Metadata

Module for IntegratingData Manager with File Manager

User Interface

Data Manager for Metadata

MultimediaFiles

Architectures:Tight Integration

User InterfaceUser Interface

MM-DBMS:Integrated data manager and file manager

MM-DBMS:Integrated data manager and file manager

MultimediaDatabase

MultimediaDatabase

Architectures:Functional

User InterfaceUser Interface

MultimediaDatabase

MultimediaDatabase

• Representation• Distribution• Quality of Service• Real-time• Heterogeneity

• Query/Update• Transactions• Metadata• Integrity/Security

StorageStorage

Data Model:Scenario

Example:Object representation

Object A2000 Frames

4/95 8/95

5/95 10/95

Object B3000 Frames

Data Model:Object

ObjectA

ObjectA

ID 2098

interval (4/95, 8/95)

contents

Frames

2000

Data Model:Object-Relational

ID Interval Contents Frame

2098 (4/95, 8/95) 2000

Functions:Editing

Example: Object editing

Editing objects A and B by merging them to form a new object over interval 4/15/95 to 8/15/95

4/15/95 8/15/95

ObjectC

Multimedia Data Access: Some approaches

Text data

- Selection with index features

- Methods: Full text scanning, Inverted files, Document clustering Audio/Speech data

- Pattern matching algorithms Matching index features given for searching and ones

available in the database Image data

- Identifying geometric boundaries, Identifying spatial relationships, Image clustering

Video data

- Retrieval with metadata, Pattern matching with images

Metadata for Multimedia

Metadata may be annotations and stored in relations

- I.e., Metadata from text, images, audio and video are extracted as stored as text

- Text metadata may be converted to relations by tagging and extracting concepts

Metadata may be images of video data

- E.g., certain frames may be captured as metadata Multimedia data understanding

- Extracting metadata from the multimedia data

Storage Methods

Single disk storage

- Objects belonging to different media types in same disk Multiple disk storage

- Objects distributed across disks Example: individual media types stored in different disks I.e., audio in one disk and video in another Need to synchronize for presentation (real-time techniques)

Multiple disks with striping

- Distribute placement of media objects in different disks Called disk striping

Security Issues

Access Control Multilevel Security Architecture Secure Geospatial Information Systems

Access Control for Multimedia Databases Access Control for Text, Images, Audio and Video Granularity of Protection

- Text John has access to Chapters 1 and 2 but not to 3 and 4

- Images John has access to portions of the image Access control for pixels?

- Video and Audio John has access to Frames 1000 to 2000 Jane has access only to scenes in US

- Security constraints Association based constraints

E.g., collections of images are classified

MLS Security

Book

Object

Introduction

Set of Sections

References

Introduction: Level = UnclassifiedSet of Sections: Level = TopSecretReferences: Level = Secret

Example Security Architecture: Integrity Lock

MultimediaDatabase

Trusted Agentto computechecksums

Sensor

Data Manager

UntrustedMultimedia DataManager

Compute ChecksumBased on say multimedia data value(such as video object content)Security level and Checksum

Compute ChecksumBased on multimedia data valueand Security level retrievedfrom the stored multimedia database

Inference Control

Metadata,Constraints

User Interface Manager

Inference EngineActs as an Inference Controller

MultimediaDatabase

MultimediaDatabaseManager

Authorization Model for Secure Geospatial Systems

Geospatial images could be Digital Raster Images that store images as pixels or Digital Vector Images that store images as points, lines and polygons

GSAM: Geospatial Authorization Model specifies subjects, credentials, objects (e.g, points, lines, pixels etc.) and the access that subjects have to objects

Reference: Authorization Model for Geospatial Data; Atluri and Chun, IEEE Transactions on Dependable and Secure Computing, Volume 1, #4, October – December 2004.

Secure Geospatial Systems

++++

Classified content blanked at the Unclassified level

++++

++++++++

++++

++++

Unclassified content++++

Directions and Challenges in Managing Multimedia Databases

Much work on data models, query languages, architectures and indexing (still need more work on indexing)

Increasing interest in

- Quality of Service for Multimedia Data Management Synchronizing audio and video Synchronizing storage retrieval and presentations Real-time scheduling techniques

- Distributed multimedia database management Query processing techniques

- Multimedia on the Web Capture, annotate, summarize, disseminate

- Mining multimedia data Extracting information previously unknown

Example: Automated Digital Capture, Analysis and Publication of Broadcast News

VideoSource Scene

ChangeDetection

SpeakerChange

Detection

SilenceDetection

CommercialDetection

Key FrameSelection

StorySegmentation

NamedEntityTagging

Broadcast News Editor (BNE) Broadcast NewsNavigator (BNN)

Video and

Metadata

MultimediaDatabase

ManagementSystem

Web-based Search/Browse by Program, Person, Location, ...

Imagery

Audio

ClosedCaptionText

Segregate VideoStreams

Analyze and Store Video and Metadata

StoryGIST Theme

FrameClassifier

ClosedCaption

Preprocess

Correlation

Token Detection

BroadcastDetection

Example Web Page

SelectStory

Elaborate on Story

KeyFrame

Source

ClosedCaption

Video

6 Most FrequentTags

SummaryRelated Web Sites

Apply QueryFlocks Data Mining Tool:(MITRE/Stanford Tool)

Extracting Relations from Text for Mining: An Example

TextCorpus Repository

ConceptExtraction

AssociationRuleProduct

Person1 Person2Natalie Allen Linden Soles 117Leon Harris Joie Chen 53Ron Goldman Nicole Simpson 19

. . .Mobotu SeseSeko

Laurent Kabila 10

Goal: FindCooperating/Combating Leadersin a territory

Image Processing:Example: Change Detection:

Trained Neural Network to predict “new” pixel from “old” pixel

- Neural Networks good for multidimensional continuous data

- Multiple nets gives range of “expected values” Identified pixels where actual value substantially outside range of

expected values

- Anomaly if three or more bands (of seven) out of range Identified groups of anomalous pixels

In Conclusion:

Multimedia data management is getting mature Numerous applications in several industries Challenge is to mine multimedia databases Work is just beginning on multimedia data mining Web provides lots of opportunities and challenges for

multimedia data management We cannot forget about security and privacy