First Edition by William Stallings and Lawrie Brown Lecture slides by Lawrie Brown Chapter 5 – Database Security.

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Presentation transcript:

First Edition by William Stallings and Lawrie Brown Lecture slides by Lawrie Brown Chapter 5 – Database Security

 constructed from tables of data  each column holds a particular type of data  each row contains a specific value these  ideally has one column where all values are unique, forming an identifier/key for that row  have multiple tables linked by identifiers  use a query language to access data items meeting specified criteria

 relation / table / file  tuple / row / record  attribute / column / field  primary key  uniquely identifies a row  foreign key  links one table to attributes in another  view / virtual table

 Structure Query Language (SQL)  originally developed by IBM in the mid-1970s  standardized language to define, manipulate, and query data in a relational database  several similar versions of ANSI/ISO standard CREATE TABLE department ( Did INTEGER PRIMARY KEY, Dname CHAR (30), Dacctno CHAR (6) ) CREATE TABLE employee ( Ename CHAR (30), Did INTEGER, SalaryCode INTEGER, Eid INTEGER PRIMARY KEY, Ephone CHAR (10), FOREIGN KEY (Did) REFERENCES department (Did) ) CREATE VIEW newtable (Dname, Ename, Eid, Ephone) AS SELECT D.Dname E.Ename, E.Eid, E.Ephone FROM Department D Employee E WHERE E.Did = D.Did

 Three Keywords:  MAC, DAC, RBAC  DBMS provide access control for database  assume have authenticated user  DBMS provides specific access rights to portions of the database  e.g. create, insert, delete, update, read, write  to entire database, tables, selected rows or columns  possibly dependent on contents of a table entry  can support a range of policies:  centralized administration  ownership-based administration  decentralized administration

 two commands:  GRANT { privileges | role } [ON table] TO { user | role | PUBLIC } [IDENTIFIED BY password] [WITH GRANT OPTION]  e.g. GRANT SELECT ON ANY TABLE TO ricflair  REVOKE { privileges | role } [ON table] FROM { user | role | PUBLIC }  e.g. REVOKE SELECT ON ANY TABLE FROM ricflair  typical access rights are:  SELECT, INSERT, UPDATE, DELETE, REFERENCES

 role-based access control work well for DBMS  eases admin burden, improves security  categories of database users:  application owner  end user  administrator  DB RBAC must manage roles and their users  cf. RBAC on Microsoft’s SQL Server

 inference detection at database design  alter database structure or access controls  inference detection at query time  by monitoring and altering or rejecting queries  need some inference detection algorithm  a difficult problem  cf. employee-salary example

 provides data of a statistical nature  e.g. counts, averages  two types:  pure statistical database  ordinary database with statistical access  some users have normal access, others statistical  access control objective to allow statistical use without revealing individual entries  security problem is one of inference

 use a characteristic formula C  a logical formula over the values of attributes  e.g. (Sex=Male) AND ((Major=CS) OR (Major=EE))  query set X(C) of characteristic formula C, is the set of records matching C  a statistical query is a query that produces a value calculated over a query set

 divide queries into parts  C = C1.C2  count(C.D) = count(C1) - count (C1. ~C2)  combination is called a tracker  each part acceptable query size  overlap is desired result

 query set overlap control  limit overlap between new & previous queries  has problems and overheads  partitioning  cluster records into exclusive groups  only allow queries on entire groups  query denial and information leakage  denials can leak information  to counter must track queries from user

 add noise to statistics generated from data  will result in differences in statistics  data perturbation techniques  data swapping  generate statistics from probability distribution  output perturbation techniques  random-sample query  statistic adjustment  must minimize loss of accuracy in results

 databases typical a valuable info resource  protected by multiple layers of security: firewalls, authentication, O/S access control systems, DB access control systems, and database encryption  can encrypt  entire database - very inflexible and inefficient  individual fields - simple but inflexible  records (rows) or columns (attributes) - best  also need attribute indexes to help data retrieval  varying trade-offs

 introduced databases and DBMS  relational databases  database access control issues  SQL, role-based  inference  statistical database security issues  database encryption