Lecture7

Data and Applications Security Developments and Directions: 

Data and Applications Security Developments and Directions Dr. Bhavani Thuraisingham The University of Texas at Dallas Lecture #7 Inference Problem - I February 6, 2006

Outline : 

Outline History Access Control and Inference Inference problem in MLS/DBMS Inference problem in emerging systems Semantic data model applications Confidentiality, Privacy and Trust Directions

History : 

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 : 

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

Access Control and Inference: 

Access Control and Inference Access control in databases started with the work in System R and Ingres Projects Access Control rules were defined for databases, relations, tuples, attributes and elements SQL and QUEL languages were extended GRANT and REVOKE Statements Read access on EMP to User group A Where EMP.Salary < 30K and EMP.Dept <> Security Query Modification: Modify the query according to the access control rules Retrieve all employee information where salary < 30K and Dept is not Security

Query Modification Algorithm: 

Query Modification Algorithm Inputs: Query, Access Control Rules Output: Modified Query Algorithm: Given a query Q, examine all the access control rules relevant to the query Introduce a Where Clause to the query that negates access to the relevant attributes in the access control rules Example: rules are John does not have access to Salary in EMP and Budget in DEPT Query is to join the EMP and DEPT relations on Dept # Modify the query to Join EMP and DEPT on Dept # and project on all attributes except Salary and Budget Output is the resulting query

Security Constraints / Access Control Rules: 

Security Constraints / Access Control Rules 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

Inference Problem in MLS/DBMS: 

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: 

Revisiting Security Constraints 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 : 

Enforcement of Security Constraints User Interface Manager Constraint Manager Security Constraints Query Processor: Constraints during query and release operations Update Processor: Constraints during update operation Database Design Tool Constraints during database design operation MLS Database MLS/DBMS

Query Algorithms: 

Query Algorithms Query is modified according to the constraints Release database is examined as to what has been released Query is processed and respond 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: 

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: 

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

Data Warehousing and Inference: 

Data Warehousing and Inference Challenge: Controlling access to the Warehouse and at the same time enforcing the access control policies enforced by the back-end Database systems Data Data Data

Data Mining as a Threat to Security: 

Data Mining as a Threat to Security Data mining gives us “facts” that are not obvious to human analysts of the data Can general trends across individuals be determined without revealing information about individuals? Possible threats: Combine collections of data and infer information that is private Disease information from prescription data Military Action from Pizza delivery to pentagon Need to protect the associations and correlations between the data that are sensitive

Security Preserving Data Mining: 

Security Preserving Data Mining Prevent useful results from mining Introduce “cover stories” to give “false” results Only make a sample of data available and that adversary is unable to come up with useful rules and predictive functions Randomization Introduce random values into the data or results; Challenge is to introduce random values without significantly affecting the data mining results Give range of values for results instead of exact values Secure Multi-party Computation Each party knows its own inputs; encryption techniques used to compute final results

Inference problem for Multimedia Databases: 

Inference problem 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

Inference Control for Semantic Web: 

Inference Control for Semantic Web According to Tim Berners Lee, The Semantic Web supports Machine readable and understandable web pages Layers for the semantic web: Security cuts across all layers Challenge: Not only integrating the layers for the semantic web, but also ensuring secure interoperability XML, XML Schemas Rules/Query Logic, Proof and Trust S E C U R I T Y Other Services RDF, Ontologies URI, UNICODE P R I V A C Y

Inference Control for Semantic Web - II: 

Inference Control for Semantic Web - II Semantic web has reasoning capabilities Based on several logics including descriptive logics Inferencing is key to the operation of the semantic web Need to build inference controllers that can handle different types of inferencing capability

Example Security-Enhanced Semantic Web: 

Example Security-Enhanced Semantic Web Security Policies Ontologies Rules Semantic Web Engine XML, RDF Documents Web Pages, Databases Inference Engine/ Inference Controller Interface to the Security-Enhanced Semantic Web Technology to be developed by project

Security and Ontologies: 

Security and Ontologies Access control for Ontologies Who can access which parts of the Ontologies E.g, Professor can access all patents of the department while the Secretary can access only the descriptions of the patents in the patent ontology Ontologies for Security Applications Use ontologies for specifying security/privacy policies Integrating heterogeneous policies may involve integrating ontologies and resolving inconsistencies

Inference Control in XML Documents: 

Inference Control in XML Documents Access control and authorization models Protecting entire documents, parts of documents, propagations of access control privileges; Protecting DTDs vs Document instances; Secure XML Schemas Inference problem for XML documents Portions of documents taken together could be sensitive, individually not sensitive

Semantic Model for Inference Control: 

Semantic Model for Inference Control Patient John Cancer Influenza Has disease Travels frequently England address John’s address Dark lines/boxes contain sensitive information Use Reasoning Strategies developed for Semantic Models such as Semantic Nets and Conceptual Graphs to reason about the applications And detect potential inference violations

Directions: 

Directions Inference problem is still being investigated Census bureau still working on statistical databases Need to find real world examples in the Military world Inference problem with respect to medial records Much of the focus is now on the Privacy problem Privacy problem can be regarded to be a special case of the inference problem