Trustworthy Semantic Web

Dr. Bhavani ThuraisinghamThe University of Texas at Dallas

Inference Problem

March 4, 2011

History Statistical databases (1970s – present) Inference problem in databases (early 1980s - present) Inference problem in MLS/DBMS (late 1980s – present) Unsolvability results (1990) Logic for secure databases (1990) Semantic data model applications (late 1980s - present) Emerging applications (1990s – present) Privacy (2000 – present)

Statistical Databases Census Bureau has been focusing for decades on statistical

inference and statistical database Collections of data such as sums and averages may be given out

but not the individual data elements Techniques include - Perturbation where results are modified - Randomization where random samples are used to compute

summaries Techniques are being used now for privacy preserving data mining

Security Constraints / Access Control Rules / Policies

Simple Constraint: John cannot access the attribute Salary of relation EMP

Content-based constraint: If relation MISS contains information about missions in the Middle East, then John cannot access MISS

Association-based Constraint: Ship’s location and mission taken together cannot be accessed by John; individually each attribute can be accessed by John

Release constraint: After X is released Y cannot be accessed by John

Aggregate Constraint: Ten or more tuples taken together cannot be accessed by John

Dynamic Constraint: After the Mission, information about the mission can be accessed by John

Security Constraints/Policies for Healthcare Simple Constraint: Only doctors can access medical records Content-based constraint: If the patient has Aids then this

information is private Association-based Constraint: Names and medical records taken

together is private Release constraint: After medical records are released, names

cannot be released Aggregate Constraint: The collection of patients is private,

individually public Dynamic Constraint: After the patient dies, information about him

becomes public

Inference Problem in MLS/DBMS

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

Revisiting Security Constraints / Policies Simple Constraint: Mission attribute of SHIP is Secret Content-based constraint: If relation MISSION contains information

about missions in Europe, then MISSION is Secret Association-based Constraint: Ship’s location and mission taken

together is Secret; individually each attribute is Unclassified Release constraint: After X is released Y is Secret Aggregate Constraint: Ten or more tuples taken together is Secret Dynamic Constraint: After the Mission, information about the

mission is Unclassified Logical Constraint: A Implies B; therefore if B is Secret then A must

be at least Secret

Enforcement of Security Constraints

User Interface Manager

ConstraintManager

Security Constraints

Query Processor:

Constraints during query and release operations

Update Processor:

Constraints during update operation

Database Design Tool

Constraints during database design operation

DatabaseData Manager

Query Algorithms

Query is modified according to the constraints Release database is examined as to what has been released Query is processed and response assembled Release database is examined to determine whether the response

should be released Result is given to the user Portions of the query processor are trusted

Update Algorithms

Certain constraints are examined during update operation Example: Content-based constraints The security level of the data is computed Data is entered at the appropriate level Certain parts of the Update Processor are trusted

Database Design Algorithms

Certain constraints are examined during the database design time- Example: Simple, Association and Logical Constraints

Schema are assigned security levels Database is partitioned accordingly Example:- If Ships location and mission taken together is Secret, then

SHIP (S#, Sname) is Unclassified, LOC-MISS(S#, Location, Mission) is Secret LOC(Location) is Unclassified- MISS(Mission) is Unclassified

Example Security-Enhanced Semantic Web

Security Policies

Ontologies

Rules

Semantic Web Engine

RDF, OWLDocumentsWeb Pages, Databases

Inference Engine/Inference Controller

Interface to the Security-Enhanced Semantic WebTechnology

to be developed by project