Inference Attacks

Phil Factor explains that your technique for pseudonymizing data doesn’t necessarily anonymize the data:

It is possible to mine data for hidden gems of information by looking at significant patterns of data. Unfortunately, this sometimes means that published datasets can reveal sensitive data when the publisher didn’t intend it, or even when they tried to prevent it by suppressing any part of the data that could enable individuals to be identified

Using creative querying, linking tables in ways that weren’t originally envisaged, as well as using well-known and documented analytical techniques, it’s often possible to infer the values of ‘suppressed’ data from the values provided in other, non-suppressed data. One man’s data mining is another man’s data inference attack.

Read the whole thing.  One big problem with trying to anonymize data is that you don’t know how much the attacker knows.  Especially with outliers or smaller samples, you might be able to glean interesting information with a series of queries.  Even if the application only returns aggregated results for some N, you can often put together a set of queries where you slice the population different ways until you get hidden details on individual.  Phil covers these types of inference attacks.

Related Posts

Service Broker Security

Colleen Morrow is back with a new item in her Service Broker series, this time on securing Service Broker implementations: There are 2 types of security in Service Broker: dialog and transport. Dialog security establishes a secure, authenticated connection between Service Broker Services or dialog endpoints. Transport security establishes an authenticated network connection between SQL […]

Read More

Everyone’s Data Is Dirty

Chirag Shivalker hits the highlights on dirty data: It might sound a bit abrupt, but clean data is a myth. If your data is dirty, so is everyone else’s. Enterprises are more than dependent on data these days, and it is going to stay the same in coming years. They need to collect data in order […]

Read More

Categories

August 2017
MTWTFSS
« Jul Sep »
 123456
78910111213
14151617181920
21222324252627
28293031