Page 590 - (ISC)² CISSP Certified Information Systems Security Professional Official Study Guide

adequately assess the potential information they may reveal to
unauthorized individuals.

Inference

The database security issues posed by inference attacks are similar to
those posed by the threat of data aggregation. Inference attacks
involve combining several pieces of nonsensitive information to gain
access to information that should be classified at a higher level.

However, inference makes use of the human mind’s deductive capacity
rather than the raw mathematical ability of modern database
platforms.

A commonly cited example of an inference attack is that of the
accounting clerk at a large corporation who is allowed to retrieve the
total amount the company spends on salaries for use in a top-level
report but is not allowed to access the salaries of individual employees.

The accounting clerk often has to prepare those reports with effective
dates in the past and so is allowed to access the total salary amounts
for any day in the past year. Say, for example, that this clerk must also
know the hiring and termination dates of various employees and has
access to this information. This opens the door for an inference attack.
If an employee was the only person hired on a specific date, the
accounting clerk can now retrieve the total salary amount on that date

and the day before and deduce the salary of that particular employee—
sensitive information that the user would not be permitted to access
directly.

As with aggregation, the best defense against inference attacks is to
maintain constant vigilance over the permissions granted to individual
users. Furthermore, intentional blurring of data may be used to
prevent the inference of sensitive information. For example, if the

accounting clerk were able to retrieve only salary information rounded
to the nearest million, they would probably not be able to gain any
useful information about individual employees. Finally, you can use
database partitioning (discussed earlier in this chapter) to help
subvert these attacks.

Data Mining and Data Warehousing