1. Data and Applications Security Developments and Directions
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Lecture #14
Secure Multimedia Data Management
March 4, 2009

2. Outline Multimedia Data Management Systems Security
Secure Geospatial data management

3. Why Multimedia Data Management System?
Need persistent storage for managing large quantities of multimedia data
A Multimedia data manager manages multimedia data such as text, images, audio, animation, video
Extended by a Browser to produce a Hypermedia data management system
Heterogeneity with respect to data types
Numerous Applications
Entertainment, Defense and Intelligence, Telecommunications, Finance, Medical

4. Architectures: Loose Integration
User Interface
Module for Integrating
Data Manager with File Manager
Data Manager for Metadata
Multimedia
File Manager
Multimedia
Files
Metadata

5. Architectures: Tight Integration
User Interface
MM-DBMS:
Integrated data manager and file manager
Multimedia Database

6. Example: Data Model: Scenario Object A 2000 Frames
Object representation
Object A 2000 Frames
4/95
8/95
5/95
10/95
Object B
3000 Frames

7. 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

8. 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

9. 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

10. Security Issues Access Control Multilevel Security Architecture
Secure Geospatial Information Systems

11. 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

12. MLS Security
Problem is that we may not know what may be learned from mining
Can’t “Classify everything”; as some is open source or may have large benefits to being accessible
This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar.
Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

13. Example Security Architecture: Integrity Lock
Problem is that we may not know what may be learned from mining
Can’t “Classify everything”; as some is open source or may have large benefits to being accessible
This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar.
Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

14. Inference Control
Problem is that we may not know what may be learned from mining
Can’t “Classify everything”; as some is open source or may have large benefits to being accessible
This is the opposite of statistical queries – we are concerned about preventing generalities from specifics, rather then specifics from generalities – but conceptually similar.
Not the same as induction – data mining finds “rules” that are generally true (high confidence and support), but not necessarily exact.

15. Securing Geospatial Data
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.
Bhavani M. Thuraisingham, Gal Lavee, Elisa Bertino, Jianping Fan, Latifur Khan: Access control, confidentiality and privacy for video surveillance databases. SACMAT 2006: 1-10
Details will be given in one of the lectures after the mid-term.

16. Directions Multimedia data security is getting some attention
Little research on Geospatial data security
Digital watermarking is getting some attention
Our focus at UTD is to develop a secure geospatial semantic web
We have developed a system called DAGIS and demonstrating secure interoperability
Details will be given later