Download 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
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, frontend

Introduction to Databases
Records – contain related group of
data
 Fields (elements) – elementary data
items
 Schema – logical structure of
database
 Subschema – view into database

Introduction to Databases

Relational
• Rows (relation); columns (attributes)
• DB2, Oracle, Access

Hierarchical
• IMS

Object-oriented
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
Advantages of Using Databases
Shared access
 Minimal redundancy
 Data consistency
 Data integrity
 Controlled access

Security Requirements
Physical database integrity
 Logical database integrity
 Element integrity
 Auditability
 Access control
 User authentication
 Availability

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

Element Integrity
Correctness or accuracy of elements
 Field checks
 Access control
 Maintain a change log – list every
change made to the database

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

Other Security Requirements
User Authentication
 Confidentiality
 Availability

Reliability and Integrity
Database integrity
 Element integrity
 Element accuracy


Some protection from OS
• File access
• Data integrity checks
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

Redundancy / Internal Consistency

Error detection / Correction codes
(parity bits, Hamming codes, CRCs)

Shadow fields

Log of user accesses and changes
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
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
Access Decisions
Need an access policy (programmed
into DBMS)
 Availability – blocking; permanent
blocking
 Acceptability of Access (sensitive
data)
 Assurance of Authenticity

Types of Disclosures

Exact Data

Bounds

Negative Results

Existence of Data

Probable Values
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

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)
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
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)
Controls

Combined Results
• Sums
• Ranges
• Rounding
Random Sample
 Random Data Perturbation
 Query Analysis – “should the result
be provided”

Conclusion on the Inference
Problem

Suppress obviously sensitive
information

Track what the user knows

Disguise the data
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

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
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
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

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
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