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Chapter 6 – Database Security

Chapter 6 – Database Security. Integrity for databases: record integrity, data correctness, update integrity Security for databases: access control, inference, and aggregation Multilevel secure databases: partitioned, cryptographically sealed, filtered. Introduction to Databases.

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Chapter 6 – Database Security

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  1. Chapter 6 – Database Security • Integrity for databases: record integrity, data correctness, update integrity • Security for databases: access control, inference, and aggregation • Multilevel secure databases: partitioned, cryptographically sealed, filtered

  2. Introduction to Databases • Database – collection of data and set of rules that organize the data by specifying certain relationships among the data • Database administrator (DBA) • Database management system (DBMS) – database manager, front-end

  3. Introduction to Databases • Records – contain related group of data • Fields (elements) – elementary data items • Schema – logical structure of database • Subschema – view into database

  4. Introduction to Databases • Relational • Rows (relation); columns (attributes) • DB2, Oracle, Access • Hierarchical • IMS • Object-oriented

  5. Introduction to Databases • Queries • SELECT NAME = ‘ADAMS’ • SELECT (ZIP = ‘43210’) ^ (NAME = ‘ADAMS’) • Project • SHOW FIRST WHERE (ZIP = ‘43210’) ^ (NAME = ‘ADAMS’) • Join • SHOW NAME, AIRPORT WHERE NAME.ZIP = AIRPORT.ZIP

  6. Advantages of Using Databases • Shared access • Minimal redundancy • Data consistency • Data integrity • Controlled access

  7. Security Requirements • Physical database integrity • Logical database integrity • Element integrity • Auditability • Access control • User authentication • Availability

  8. Integrity of the Database • Users must be able to trust the accuracy of the data values • Updates are performed by authorized individuals • Integrity is the responsibility of the DBMS, the OS, and the computing system manager • Must be able to reconstruct the database at the point of a failure

  9. Element Integrity • Correctness or accuracy of elements • Field checks • Access control • Maintain a change log – list every change made to the database

  10. Auditability & Access Control • Desirable to generate an audit record of all access to the database (reads/writes) • Pass-through problem – accessing a record or element without transferring the data received to the user (no reads/writes) • Databases separated logically by user access privileges

  11. Other Security Requirements • User Authentication • Confidentiality • Availability

  12. Reliability and Integrity • Database integrity • Element integrity • Element accuracy • Some protection from OS • File access • Data integrity checks

  13. Two-Phase Update • Failure of computing system in middle of modifying data • Intent Phase – gather resources needed for update; write commit flag to the database • Update Phase – make permanent changes

  14. Redundancy / Internal Consistency • Error detection / Correction codes (parity bits, Hamming codes, CRCs) • Shadow fields • Log of user accesses and changes

  15. Concurrency/Consistency • Access by two users sharing the same database must be constrained (lock) • Monitors –check entered values to ensure consistency with rest of DB • Range Comparisons • State Constraints – describes condition of database (unique employee #) • Transition Constraints – conditions before changes are applied to DB

  16. Sensitive Data • Data that should not be made public • What if some but not all of the elements of a DB are sensitive • Inherently sensitive • From a sensitive source • Declared sensitive • Part of a sensitive attribute or record • Sensitive in relation to previously disclosed information

  17. Access Decisions • Need an access policy (programmed into DBMS) • Availability – blocking; permanent blocking • Acceptability of Access (sensitive data) • Assurance of Authenticity

  18. Types of Disclosures • Exact Data • Bounds • Negative Results • Existence of Data • Probable Values

  19. Security vs. Precision • Aim to protect all sensitive data while revealing as much nonsensitive data as possible • Want to maintain perfect confidentiality with maximum precision

  20. Inference • Way to infer / derive sensitive data from nonsensitive data • Direct Attack • List NAME where SEX=M ^ DRUGS=1 • List NAME where (SEX=M ^ DRUGS=1) v (SEX#M ^ SEX#F) v (DORM=AYRES)

  21. Indirect Attack • Sum • Show STUDENT-AID WHERE SEX=F ^ DORM=Grey • Count • Show Count, STUDENT-AID WHERE SEX=M ^ DORM=Holmes • List NAME where (SEX=M ^ DORM=Holmes) • Median • Tracker Attacks – using additional queries that produce small results

  22. Controls • Suppression – don’t provide sensitive data • Concealing – don’t provide actual values (“close to”) • Limited Response Suppression • n-item k-percent rule eliminates low frequency elements from being displayed (may need to suppress additional rows/columns)

  23. Controls • Combined Results • Sums • Ranges • Rounding • Random Sample • Random Data Perturbation • Query Analysis – “should the result be provided”

  24. Conclusion on the Inference Problem • Suppress obviously sensitive information • Track what the user knows • Disguise the data

  25. Aggregation • Building sensitive results from less sensitive inputs • Data mining – process of sifting through multiple databases and correlating multiple data elements to find useful information

  26. Multilevel Databases • Differentiated Security • Security of single element may be different from security of other elements • Two levels – sensitive and nonsensitive are inadequate to represent some security situations • Security of an aggregate (sum, count,…) may be different from security of the individual elements • Granularity

  27. Security Issues • Integrity • *-property for access control • Either process cleared at a high level cannot write to a lower level or process must be a “trusted process” • Confidentiality • Different users at different levels may get different query results • Polyinstantiation – record can appear more than once with different levels of confidentiality

  28. Proposals for Multilevel Security • Separation • Partitioning – divide DB into separate DBs with own level of sensitivity • Encryption (time consuming) • Integrity Lock – each data item contains a sensitivity label and a checksum • Sensitivity label must be unforgeable, unique, concealed • Checksum must be unique • Sensitivity lock

  29. Design of Multilevel Secure Databases • Integrity Lock – not efficient (space/time) • Trusted Front-end (Guard) – does authentication and filtering • Commutative Filters – • screen user’s requests, reformats, so that only appropriate data is returned

  30. Design of Multilevel Secure Databases • Distributed (federated) database • Trusted front-end controls access to two DBMSs – one for high-sensitivity data and one for low-sensitivity data • Very complex • Window/View • Subset of a database containing exactly the information that the user is entitled to access

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