Database Systems
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12-10-2010, 10:46 AM

.doc   DATA PROTECTION.doc (Size: 40 KB / Downloads: 60)
Database Systems

Inference Detection in Database Systems:
1. A threat to database security is the misuses of these databases by the authorized users, for example selling the personal information to outsiders. Various access control mechanisms have been proposed for protecting individual information in statistical database systems.

2. These mechanisms are specifically designed for statistical databases, making them not applicable to general purpose database systems. In multilevel secure database systems, a type of attacks called inference is identified. An inference occurs when a user uses legitimate data to infer information without directly accessing it. Existing approaches to inference detection focus on analyzing functional dependencies in the database schema. However, it is possible to exploit data level functional dependencies to achieve inferences. For example, although in general the job title does not functionally determine salary (different vice-presidents may earn different salaries), the dependency may hold for lower rank jobs.

3. This research investigates the detection of attempts to access personal information in relational database systems. We identify five types of inferences: unique characteristic, logical implication, complementary, overlapping, and functional dependency. Algorithms for these inferences are developed. These inferences are detected by auditing both user queries and their return tables.

4. In general the inference problem is an NP-complete problem (for example, determining the equivalence between two logical expressions). We have no attempt to completely detect all possible types of inferences. The detection system essentially makes the inference attacks more difficult. This might result in having the user to issue more queries, which then could be detected by anomaly detection techniques.

5. NP is the set of decision problems solvable in polynomial time by a non-deterministic Turing machine.

6. Anomaly Detection refers to detecting patterns in a given data set that do not conform to an established normal behavior Bayesian network: A Bayesian network (or a belief network) is a probabilistic graphical model that represents a set of variables and their probabilistic independencies. For example, a Bayesian network could represent the probabilistic relationships between diseases and symptoms. Given symptoms, the network can be used to compute the probabilities of the presence of various diseases. Formally, Bayesian networks are directed acyclic graphs whose nodes represent variables, and whose missing edges encode conditional independencies between the variables. Nodes can represent any kind of variable, be it a measured parameter, a latent variable or a hypothesis.
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12-10-2010, 10:54 AM

.pdf   dbms.pp.pdf (Size: 645.83 KB / Downloads: 51)
Database Management System
Protection Profile

lowing threats are countered by the DBMS.
T.ACCESS Unauthorised Access to the Database. An outsider or system user who is not (currently)
an authorised database user accesses the DBMS. This threat includes: Impersonation -
a person, who may or may not be an authorised database user, accesses the DBMS, by
impersonating an authorised database user (including an authorised user impersonating
a different user who has different - possibly more privileged - access).
T.DATA Unauthorised Access to Information. An authorised database user accesses information
contained within a DBMS without the permission of the database user who owns or
who has responsibility for protecting the data.
32 This threat includes unauthorised access to DBMS information, residual information
held in memory or storage resources managed by the TOE, or DB control data.
T.RESOURCE Excessive Consumption of Resources. An authenticated database user consumes global
database resources, in a way which compromises the ability of other database users to
access the DBMS.
33 This represents a threat to the availability of the information held within a DBMS. For
example, a database user could perform actions which could consume excessive
resources, preventing other database users from legitimately accessing data, resources
and services in a timely manner. Such attacks may be malicious, inconsiderate or
careless, or the database user may simply be unaware of the potential consequences of
his actions. The impact of such attacks on system availability and reliability would be
greatly amplified by multiple users acting concurrently.
T.ATTACK Undetected Attack. An undetected compromise of the DBMS occurs as a result of an
attacker (whether an authorised user of the database or not) attempting to perform
actions that the individual is not authorised to perform.
34 This threat is included because, whatever countermeasures are provided to address the
other threats, there is still a residual threat of a violation of the security policy occurring
by attackers attempting to defeat those countermeasures.
T.ABUSE.USER Abuse of Privileges. An undetected compromise of the DBMS occurs as a result of a
database user (intentionally or otherwise) performing actions the individual is
authorised to perform.
35 This threat is included because, whatever countermeasures are provided to address the
other threats, there is still a residual threat of a violation of the security policy occurring,
or the database being placed at risk, as a result of actions taken by authorised
database users. For example a database user may grant access to a DB object they are
responsible for to another database user who is able to use this information to perform
a fraudulent action.
36 Note that this threat does not extend to highly trusted database users: see the assumption
A.MANAGE below.
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13-10-2010, 12:22 PM

.ppt   BD_1_06.ppt (Size: 191 KB / Downloads: 71)
Database Systems

Basic Terminology

Data – raw facts
Field – a character or group of characters (alphanumeric or numeric) that has a specific meaning
Record – a set of one or more logically related fields
File – a set of related records
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27-08-2012, 12:27 PM

Database Systems

.ppt   Database.ppt (Size: 85.5 KB / Downloads: 10)

Non-Relational Systems

Database systems can be conveniently categorized according to the data structures and operators they present to the user
The oldest system fall into three broad categories:
Flat-File Systems
Hierarchical Systems
Network Systems
We will not discuss these categories in great detail as, although some are still used, they are obsolete
We will focus on relational databases

Flat-File Systems

The file system was a start. However, it was seriously inefficient
Essentially, in order to find a record, someone would have to read through the entire file and hope it would not be the last record
Imagine last record is the required one…
What was needed, computer scientist thought, was a “card catalog”, a mean to achieve the ability to efficiently access a single record without searching the entire file to find it
So the result was the “indexed file-oriented systems” in which a single index file stored “key” words and pointers to records that were stored else where. This made retrieval much more easier.
But How???
To find data, one needed only search for keys rather than reading entire records.
This was referred to as “flat-file systems”

Hierarchical Databases

Early databases were Hierarchical databases
Records are arranged in top-down structure, that resembles a tree
They used a hierarchical structure, in which all related data had a “parent-to-child relationship”
A parent data item could have multiple child data items, but a child could only have one parent
Relationship between related data were created using pointers, which are links to the physical locations where data was written to the disk

Parent/Child Relationship

The “parent/child” rule assures that data are systematically accessible . To get to a low-level data, you start at the root and work your way down through the tree until you reach your target
One problem with this system is that the user must know how the tree is structured in order to find anything!!
The hierarchical data model, however, is much more efficient then the flat-file model discussed earlier because there is not as much need for redundant data. If a change in the data is necessary, the change might be need to processed once
One other feature of the hierarchical database system was the introduction of the Database Management System (DBMS) software


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