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

Outline Multimedia Data Management Systems Security Secure Geospatial data management

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

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

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

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

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 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.

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.

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.

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.

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