Transcript 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
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Introduction to Databases
Records – contain related group of
data
 Fields (elements) – elementary data
items
 Schema – logical structure of
database
 Subschema – view into database
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Introduction to Databases
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Relational
• Rows (relation); columns (attributes)
• DB2, Oracle, Access
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Hierarchical
• IMS
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Object-oriented
Introduction to Databases
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Queries
• SELECT NAME = ‘ADAMS’
• SELECT (ZIP = ‘43210’) ^ (NAME = ‘ADAMS’)
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Project
• SHOW FIRST WHERE (ZIP = ‘43210’) ^ (NAME
= ‘ADAMS’)
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Join
• SHOW NAME, AIRPORT WHERE
NAME.ZIP = AIRPORT.ZIP
Advantages of Using Databases
Shared access
 Minimal redundancy
 Data consistency
 Data integrity
 Controlled access
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Security Requirements
Physical database integrity
 Logical database integrity
 Element integrity
 Auditability
 Access control
 User authentication
 Availability
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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
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Element Integrity
Correctness or accuracy of elements
 Field checks
 Access control
 Maintain a change log – list every
change made to the database
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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
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Other Security Requirements
User Authentication
 Confidentiality
 Availability
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Reliability and Integrity
Database integrity
 Element integrity
 Element accuracy
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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
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Redundancy / Internal Consistency
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Error detection / Correction codes
(parity bits, Hamming codes, CRCs)
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Shadow fields
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Log of user accesses and changes
Concurrency/Consistency
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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
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Types of Disclosures
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Exact Data
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Bounds
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Negative Results
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Existence of Data
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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
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Inference
Way to infer / derive sensitive data
from nonsensitive data
 Direct Attack
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• List NAME where SEX=M ^ DRUGS=1
• List NAME where (SEX=M ^ DRUGS=1)
v (SEX#M ^ SEX#F) v (DORM=AYRES)
Indirect Attack
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Sum
• Show STUDENT-AID WHERE SEX=F ^
DORM=Grey
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Count
• Show Count, STUDENT-AID WHERE SEX=M ^
DORM=Holmes
• List NAME where (SEX=M ^ DORM=Holmes)
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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
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• n-item k-percent rule eliminates low
frequency elements from being
displayed (may need to suppress
additional rows/columns)
Controls
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Combined Results
• Sums
• Ranges
• Rounding
Random Sample
 Random Data Perturbation
 Query Analysis – “should the result
be provided”
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Conclusion on the Inference
Problem
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Suppress obviously sensitive
information
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Track what the user knows
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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
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Multilevel Databases
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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
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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”
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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
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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
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Design of Multilevel Secure
Databases
Integrity Lock – not efficient
(space/time)
 Trusted Front-end (Guard) – does
authentication and filtering
 Commutative Filters –
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• screen user’s requests, reformats, so
that only appropriate data is returned
Design of Multilevel Secure
Databases
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Distributed (federated) database
• Trusted front-end controls access to two
DBMSs – one for high-sensitivity data
and one for low-sensitivity data
• Very complex
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Window/View
• Subset of a database containing exactly
the information that the user is entitled
to access