1. DATABASE MODELING AND
SECURITY

2. WHAT IS DATA MODELING?
 Data modeling is the act of exploring data-oriented
structures.
 Define key data modeling terms
 Entity type
 Attribute
 Multivalued attribute
 Relationship
 Degree
 Cardinality
 Business Rule
 Associative entity
 Trigger
 Supertype
 Subtype

3. THE DATA MODELING PROCESS

4. HOW ARE DATA MODELS USED IN PRACTICE?
 Conceptual data models- These are often created
as part of initial requirements envisioning efforts to
explore the high-level static business structures and
concepts.
 Logical data models-used to explore the domain
concepts, and their relationships, of problem
domain.
 Physical data models (PDMs)-PDMs are used to
design the internal schema of a database, depicting
the data tables, the data columns of those
tables, and the relationships between the tables.

5. A SIMPLE LOGICAL DATA MODEL.

6. A SIMPLE PHYSICAL DATA MODEL

7. WHAT ABOUT CONCEPTUAL MODELS?
 Object-Role models(ORM’s) are preferably created
for conceptual models.

8. COMMON DATA MODELING NOTATIONS

9. CONT.

10. CONT.

11. HOW TO MODEL DATA
 The following tasks are performed in an iterative
manner
 Identify entity types
 Identify attributes
 Apply naming conventions
 Identify relationships
 Apply data model patterns
 Assign keys
 Normalize to reduce data redundancy
 Denormalize to improve performance

12.  1.Identify Entity Types
 Entity - a class of real world objects having common
characteristics and properties about which we wish to
record information.An entity can be of normal type or
weak type.
 2.Identify Attributes
 Attribute - a characteristic of an entity or relationship
 * Identifier - uniquely determines an instance of an entity
 * Identity dependence - when a portion of an identifier is
inherited from another entity
 * Multi-valued - same attribute having many values for
one entity
 * Surrogate - system created and controlled unique key

13.  3. Apply Data Naming Conventions- Every
organization should have standards and guidelines
applicable to data modeling, something we should be
able to obtain from your enterprise administrator.
 Identify Relationships
 Re lat ionship - an association among two or more
entities
 * occurrence - instance of a relationship is the collective
instances of the related entities
 * degree - number of entities associated in the
relationship (binary, ternary, other n-ary)
 * connectivity - one-to-one, one-to-many, many-to-many
 * existence dependency (constraint) -
optional/mandatory

14. A LOGICAL DATA MODEL

15. 5.APPLY DATA MODEL PATTERNS

16.  6.Assign keys-

17. 7. NORMALIZE TO REDUCE DATA REDUNDANCY
 The goal of data normalization is to reduce and
even eliminate data redundancy.
 Table 2. Data Normalization Rules.
 First normal form (1NF)-An entity type is in 1NF
when it contains no repeating groups of data.
 Second normal form (2NF)-An entity type is in
2NF when it is in 1NF and when all of its non-key
attributes are fully dependent on its primary key.
 Third normal form (3NF)-An entity type is in 3NF
when it is in 2NF and when all of its attributes are
directly dependent on the primary key.

20. 8. DENORMALIZE TO IMPROVE PERFORMANCE
 The rules of data normalization focus on reducing
data redundancy, not on improving performance of
data access. An important part of data modeling is
to denormalize portions of your data schema to
improve database access times.

21. DATABASE SECURITY

22. CONTENTS
 Definitions
 Countermeasures
 Security Controls
 Data Protection and Privacy
 Statistical Databases
 Web Database Security Issues and Solutions
 SQL Injection

23. DATABASE SECURITY DEFINITION
 Definition :
 The protection of the database against
intentional or unintentional threats using
computer-based or non-computer-based
controls
 Areas in which to reduce risk:
 theft and fraud
 loss of confidentiality
 loss of privacy
 loss of integrity
 loss of availability

24. COUNTERMEASURES
 Ways to reduce risk
 Include
 Computer Based Controls
 Non-computer Based Controls

25. COMPUTER BASED CONTROLS
 Security of a DBMS is only as good as the OS
 Computer based Security controls available:
 authorization and authentication
 views
 backup and recovery
 Integrity
 Encryption
▪ within database and data transport
 RAID – for fault tolerance
 associated procedures
▪ e.g. backup, auditing, testing, upgrading, virus checking

26. NON-COMPUTER BASED CONTROLS
 Include:
 Security policy and contingency plan
 personnel controls
 secure positioning of equipment
 escrow agreements
 maintenance agreements
 physical access controls
 Both internal and external

27. DATA SECURITY
 Two(original) broad approaches to data
security:
 Discretionary access control
 a given user has different access rights (privileges) on
different objects
 flexible, but limited to which rights users can have on an
object
 privileges can be passed on at user’s discretion
 Mandatory access control
 each data object is labelled with a certain classification
level
 each user is given a certain clearance level
 rigid, hierarchic

28. ROLE BASED ACCESS CONTROL
A specific function within an organisation
 Authorizations are granted to the roles
 Instead of users
 Users are made members of roles
 Privileges can not be passed on to other
users
 Simplifies authorization management
 Supported in SQL

29. SYSTEM R AUTHORIZATION MODEL
 One
of the first authorization model for
RDBMS
 As part of System R RDBMS
 Based on concept of ‘Protection Objects’
 Tables and views
 Access modes
 SELECT
 INSERT
 DELETE
 UPDATE
 Not all applicable for views

30. SYSTEM R AUTHORIZATION MODEL
 Userscan give access to other users
through use of
 GRANT and REVOKE
 Removing REVOKE is recursive
 System R has a closed world policy
 If no authorization then access is denied
 However authorization can be granted later
 Negative authorization
 Denials are expressed
 Denials take precedence

31. SQL FACILITIES
 SQL supports discretionary access control using
view mechanism and authorization system
 e.g. CREATE VIEW S_NINE_TO_FIVE AS
SELECT S.S#, S.SNAME, S.STATUS, S.CITY
FROM S
WHERE to_char(SYSDATE, 'HH24:MI:SS‘) >=
‘09:00:00’
AND to_char(SYSDATE, 'HH24:MI:SS‘) <= ‘17:00:00’;
GRANT SELECT, UPDATE (STATUS)
ON S_NINE_TO_FIVE
TO Purchasing;
 parameterised view
 Also referential and entity integrity

32. ORACLE SECURITY
 Oracle supports 2 types of privileges
 System privileges
 Rights to perform action on schema objects
 e.g. create table spaces, create and delete users
 Object priviliges
 Rights to perform actions on database objects
 e.g. create/delete tables, views, indexes, functions
 Priviliges can be granted to users or roles

33. ORACLE OBJECT PRIVILEGES
 Table Privileges
 ALTER, DELETE, INDEX, INSERT, REFERENC
ES, SELECT, UPDATE
 View Privileges
 DELETE, INSERT, SELECT, UPDATE
 Privileges can be granted to users or
roles, e.g.
CREATE ROLE admin;
GRANT INSERT ON my_table TO admin;
GRANT admin TO fred;
 To revoke/remove roles:
REVOKE admin FROM barney;
DROP ROLE admin;

34. ORACLE VIRTUAL PRIVATE DATABASES
 Fine-grained access control based on tuple-level
access
 Uses dynamic query modification
 Users are given a specific policy
 The policy returns a specific WHERE clause in the
query depending on the policy
 SELECT * FROM prop_for_rent
 Becomes
 SELECT * FROM prop_for_rent WHERE prop_type = ‘F’

35. DATA PROTECTION AND PRIVACY
 Privacy
 concerns the right of an individual not to have personal
information collected, stored and disclosed either
willfully or indiscriminately
 Data Protection Act
 the protection of personal data from unlawful
acquisition, storage and disclosure, and the provision
of the necessary safeguards to avoid the destruction or
corruption of the legitimate data held
 New Freedom of Information Act

36. STATISTICAL DATABASES
A database that permits queries that derive
aggregated information (e.g. sums,
averages)
 but not queries that derive individual information
 Tracking
 possible to make inferences from legal queries to
deduce answers to illegal ones
 SELECT COUNT(*) FROM STATS X WHERE X.SEX=‘M’ AND
X.OCCUPATION = ‘Programmer’
 SELECT SUM(X.SALARY) FROM STATS X WHERE X.SEX=‘M’
AND X.OCCUPATION = ‘Programmer’

37. SIMPLE EXAMPLE
 The following warehouse relation contains information
about a number of drivers, and the points they have
stored in races.
 The only queries allowed are those which utilise
aggregate operators, e.g. using count to find out a
driver’s total earnings in any one year.
 However using this table, statistical tracking is possible.
 Explain why?
DriverId Race PointsScored PrizeMoney
1 Monaco 10 50000
1 Imola 4 25000
2 Monaco 6 30000
3 Monaco 8 40000
3 Silverstone 10 50000

38. STATISTICAL DATABASES
 Various strategies can be used to minimize
problems
 prevent queries from operating on only a few
database entries
 swap attribute values among tuples
 randomly add in additional entries
 use only a random sample
 maintain history of query results and reject
queries that use a high number of records
identical to previous queries

39. WEB DATABASE SECURITY ISSUES
 Internet is an open network
 traffic can easily be monitored, e.g. credit card
numbers
 Challengeis to ensure that information
conforms to:
 privacy, integrity, authenticity, non-
fabrication, non-repudiation
 Information also needs protected on web
server
 Also need to protect from executable
content

40. WEB DATABASE SECURITY SOLUTIONS
 Various methods can be used
 proxy servers
 improve performance and filter requests
 firewalls
 prevents unauthorised access to/from a private network
 digital certificates
 electronic message attachments to verify that user is
authentic
 Kerberos
 centralised security server for all data and resources on
network

41. WEB DATABASE SECURITY SOLUTIONS
 Secure Sockets Layer and Secure HTTP
▪ SSL - secure connection between client and server
▪ S-HTTP - individual messages transmitted securely
 Secure Electronic Transactions
▪ certificates which splits transactions so that only
relevant information is provided to each user
 Java - Java Virtual Machine (JVM)
▪ class loader - checks applications do not violate system
integrity by checking class hierarchies
▪ bytecode verifier - verify that code will not crash or
violate system integrity
 ActiveX -
▪ uses digital signatures, user is responsible for security

42. SQL INJECTION
 ‘a technique used to take advantage of non-
validated input vulnerabilities to pass SQL
commands through a Web application for execution
by a backend database’1
 Can chain SQL commands
 Embed SQL commands in a string
 Ability to execute arbitrary SQL queries

43. SQL INJECTION: EXAMPLE 1
 Form asking for username and password
 Original Query:
 SQLQuery = “SELECT count(*) FROM
users WHERE username = „” +
$usename + “„ AND password = „” +
$password + “„;”
 Specify usename and password = ‘ OR “
1=1 ‘
 SELECT count(*) FROM users WHERE
username = ‘’ OR 1 = 1 AND password
= ‘’ OR 1 = 1;

44. SQL INJECTION : EXAMPLE 2
 SQLQuery = “SELECT * FROM staff WHERE
staff_no = ” + $name + “;”
 Enter staff_no: 100 OR 1 = 1
 Will give the query:
 SELECT * FROM staff WHERE staff_no =
100 OR 1 = 1;
 Even worse:
 Enter staff_no: 100; DROP TABLE staff;
SELECT * FROM sys.user_tables
 Enter staff_no: 100 UNION SELECT SELECT
Username, Password FROM Users

45. SQL INJECTION : REMEDIES
 Can include:
 Strip quotation marks and other spurious characters
from strings
 Use stored procedures
 Limit field lengths or even don’t allow text entries
 Restrict UNION

46. THE END