1. Databases Collections of data. Set of rules to organize data. Types ◦ Relational: use (rows) & columns to organize. ◦ Object oriented: complex data (audio, video) ◦ Hierarchical Model  Tree structure. ◦ Network  Hierarchical but elements have multiple parent and child records.

2. Database Characteristics Centralizes data. Allows for easier backups. Provides transaction persistence. Allows for more consistency ◦ All data at one location Provides recovery and fault tolerance. Allows sharing of data with many users. Provides security controls. ◦ Integrity checking. ◦ Access control ◦ Confidentiality

3. Database Jargon Record: collection of related data items. File: collection of records of the same type. Database: cross-referenced collection of data. DBMS: Manages and controls the database. Tuple: a row in a 2 dimensional database. Attribute: a column in a 2 dimensional database. View: virtual relation defined by DBA in order to keep subjects from viewing certain data.

4. Database Jargon continued Primary Key: Columns that make each row unique Foreign key: attribute of one table that is related to the primary key of another table. Cell: an intersection of a row and column. Schema: defines the structure of the database. Data Dictionary: central repository of data elements and their relationships. Normalization: minimize redundancy.

5. Database Table

6. ACID Rules Atomicity: all modification take effect or none take effect. Consistency: follow integrity policy for a database Isolation: transactions complete in isolation until completed without interacting with other transactions. Durability: once a transaction is verified it is committed and cannot be rolled back.

7. Schema

8. Database Replication Integrity is primary concern. ◦ Update one table, update them all Locking ◦ Prevent 2 processes from updating a record. Replication ◦ Mirror a live database allowing simultaneous reads and writes. Shadowing ◦ Mirrors all changes made to the primary database but no one accesses it.

9. Sample Network

10. Database APIs Open Database Connectivity (ODBC) ◦ API to connect to databases. Object Linking and Embedding Database (OLE DB) ◦ Replacement for ODBC, extending it. ActiveX Data Objects (ADO). ◦ Set of COM objects for accessing data sources. Java Database Connectivity (JDBC) ◦ Functionality like ODBC for Java.

11. Figure 6-2 Results of Select-Project-Join Query. Select NAME, AIRPORT from Project:NAME-ZIP, Project:ZIP- AIRPORT where NAME-ZIP:ZIP == ZIP-AIRPORT:ZIP

12. Database Integrity Concurrency: two people drop same file on a data server with different information. ◦ Databases lock tables to prevent this. Semantic Integrity: ensure structural and semantic rules are enforced. Referential Integrity: all foreign keys reference a primary key.

13. Advantages of Databases Shared Access to data centralized. Minimal redundancy: normalization. Data consistency: change of data affects all users. Data integrity: protect data. Controlled Access: authorized users only.

14. Database Integrity Entity Integrity: tuples are uniquely identified by primary key values. Rollback: ends a current transaction and cancels the changes to the database. Commit: complete a transaction and executes all changes made by a user.

15. Database Security Issues Aggregation: act of combining information from separate less sensitive sources, forming new information. Inference: a subject deduces the full story from pieces learned from aggregation.

16. Figure 6-3 Security versus Precision. Security (confidentiality): reject any query requesting a sensitive field. Precision: protect all sensitive data but reveal as much non-sensitive data as possible. Want perfect confidentiality with maximum precision.

17. Database Defense Content-dependent access control ◦ Based on sensitivity of data. Context-dependent access control ◦ Software knows what to do based upon what should be allowed based upon state and sequence of request. Cell Suppression: hide specific cells to prevent inference attacks. Partitioning: divide database into parts. Noise and perturbation: insert bogus information to misdirect and confuse. Database View: limit what each user can see.

18. Inference Attacks Direct Attack ◦ Attempt to find values by making a query that will return very few results. ◦ List Name where SEX=M and DRUGS=1 Indirect Attack ◦ Infer a final result based upon statistical results. ◦ Sum, Count, Mean, Median, Min, Max.

19. Inference (if allowed)

20. Inference Attacks Tracker Attack ◦ Fool database manager into locating the desired data by using additional queries that produce small results.  Uses logic & algebra to learn information.  Count (sex=F) – ((sex=F)&(race!=Caucasian) or (dorm!=holmes)) = 1

21. Inference Defense Apply controls to queries or Apply controls to individual items. Suppression: sensitive values not provided. ◦ Query is rejected without a response. Concealing ◦ provide an answer close to the value but not the actual value.

22. Inference Rules 1. Suppress obviously sensitive information. ◦ Error on the side of suppression. 2. Track what the user knows. ◦ Expensive ◦ Must maintain information on all user queries. ◦ What if two people working together? 3. Disguise the data. ◦ Random perturbation and rounding of data.

23. Aggregation Related to inference. ◦ Build sensitive results from less sensitive information. Must track results which user has seen. ◦ Conceal anything that might lead a user to derive a more sensitive result. Difficult to counter. ◦ Collusion: 2 people could work together.

24. Multilevel Databases Human Resource Databases contain sensitive information ◦ Salary. ◦ Last performance review. Security of a single element is different from other elements. Two levels, sensitive & non-sensitive may not be enough ◦ There are ranges of allowable knowledge. Security of combining elements may differ than that of an individual element. These 3 issues similar to military sensitivity levels.

25. Differentiated Security limit who can see what

26. Partition Database separate sensitive information

27. Database Encryption Need to know key to decipher information. Each sensitivity level has an appropriate key strength. Users with access might be able to decipher key and control database if single key used. Defense: encrypt each record with a different key.

28. Figure 6-5 Cryptographic Separation: Different Encryption Keys. Use different keys for each record to prevent leaking information.

29. Figure 6-6 Cryptographic Separation: Block Chaining. Encryption dependant upon previous blocks.

30. Figure 6-7 Integrity Lock. U.S.A.F. stored in plaintext

31. Figure 6-8 Cryptographic Checksum. Used for error detection.

32. Figure 6-9 Sensitivity Lock. Unique identifier and the sensitivity level. Each lock belongs to one record.

33. Figure 6-10 Trusted Database Manager. Integrity lock database. Only the access procedure needs to be trusted and would grant access to sensitive data. Inefficient.

34. Figure 6-11 Trusted Front End. Front end authenticates and authorizes access to data Issues Queries to DBMS. Verifies sensitivity levels and who wants access.

35. Figure 6-12 Commutative Filters. Screens and reformats user input and verifies user has permissions to access information.

36. Database Security Views: create different views for different users to limit access to part of the DB. Windows: a subset of a database. ◦ Contains information user is allowed to see. Polyinstantiation ◦ Create custom records made from full records which depend upon a user’s access level.

37. Figure 6-13 Secure Database Decomposition. Reference monitor employs Bell-LaPuda access controls. Second level provides indexing. Third layer translates views into the base relations. These 3 layers make up the TCB Perimeter. Remaining layers typical DBMS functions and user interface.

38. Data Warehousing Combine data from multiple databases. Can be petabytes of data. Used for data analysis and inference. Data Mining ◦ Process of analyzing data in search of common patterns. ◦ Search for signs of fraud  credit card companies