1. Building Trustworthy Semantic Webs
Dr. Bhavani Thuraisingham
The University of Texas at Dallas
Multilevel Secure Data Management and its implications to Multilevel semantic web technologies
October 27, 2008

2. Outline What is an MLS/DBMS? Summary of Developments Challenges
Data Models
Implications for semantic web

3. Overview of MLS/DBMS What is an MLS/DBMS Need for an MLS/DBMS
Users are cleared at different security levels
Data in the database is assigned different sensitivity levels--multilevel database
Users share the multilevel database
MLS/DBMS is the software that ensures that users only obtain information at or below their level
In general, a user reads at or below his level and writes at his level
Need for an MLS/DBMS
Operating systems control access to files; coarser grain of granularity
Database stores relationships between data
Content, Context, and Dynamic access control
Traditional operating systems access control to files is not sufficient
Need multilevel access control for DBMSs

4. Summary of Developments
Early Efforts 1975 – 1982; example: Hinke-Shafer approach
Air Force Summer Study, 1982
Research Prototypes (Integrity Lock, SeaView, LDV, etc.); Present
Trusted Database Interpretation; published 1991
Commercial Products; Present

5. Taxonomy for MLS/DBMSs
Integrity Lock Architecture: Trusted Filter; Untrusted Back-end, Untrusted Front-end. Checksum is computed by the filter based on data content and security level. Checksum recomputed when data is retrieved.
Operating Systems Providing Access Control/ Single Kernel: Multilevel data is partitioned into single level files. Operating system controls access to the filed
Extended Kernel: Kernel extensions for functions such as inference and aggregation and constraint processing
Trusted Subject: DBMS provides access control to its own data such as relations, tuples and attributes
Distributed: Data is partitioned according to security levels; In the partitioned approach, data is not replicated and there is one DBMS per level. In the replicated approach lower level data is replicated at the higher level databases

6. Integrity Lock

7. Operating System Providing Mandatory Access Control

8. Extended Kernel

9. Trusted Subject

10. Distributed Approach - I

11. Distributed Approach II

12. Some Challenges: Inference Problem
Inference is the process of forming conclusions from premises
If the conclusions are unauthorized, it becomes a problem
Inference problem in a multilevel environment
Aggregation problem is a special case of the inference problem - collections of data elements is Secret but the individual elements are Unclassified
Association problem: attributes A and B taken together is Secret - individually they are Unclassified

13. Some Challenges: Polyinstantiation
Mechanism to avoid certain signaling channels
Also supports cover stories
Example: John and James have different salaries at different levels

14. Some Challenges: Covert Channel
Database transactions manipulate data locks and covertly pass information
Two transactions T1 and T2; T1 operates at Secret level and T2 operates at Unclassified level
Relation R is classified at Unclassified level
T1 obtains read lock on R and T2 obtains write lock on R
T1 and T2 can manipulate when they request locks and signal one bit information for each attempt and over time T1 could covertly send sensitive information to T1

15. Multilevel Secure Data Model: Classifying Databases

16. Multilevel Secure Data Model: Classifying Relations

17. Multilevel Secure Data Model: Classifying Attributes/Columns

18. Multilevel Secure Data Model: Classifying Tuples/Rows

19. Multilevel Secure Data Model: Classifying Elements

20. Multilevel Secure Data Model: Classifying Views

21. Multilevel Secure Data Model: Classifying Metadata

22. Status and Directions
MLS/DBMSs have been designed and developed for various kinds of database systems including object systems, deductive systems and distributed systems
Provides an approach to host secure applications
Can use the principles to design privacy preserving database systems
Challenge is to host emerging secure applications including semantic web technologies (MLS XML, MLS RDF etc.)

23. Multilevel Semantic Web Technologies
Take RDF as an example
What so we classify for RDF?: Triples?
Security properties for RDF Schema
Query modification with SPARQL
Inference problem based on RDF data and reasoning
Design of the system – Trusted subject?
Extra credit assignment: Design of Multilevel RDF Data Store
Potential opportunity for RA position for Spring semester – to implement the design.