What this blog is about

All of us leave traces in the data that we create, either intentionally or as a side-effect of the things we do in the world — walking in front of a CCTV camera, turning on a cell phone, or whatever.

Lots of this data is analyzed, for example by businesses that want to build a relationship to customers.

I’m interested in the special case where some of the people about whom data is collected want to hide their existence, what they are like, and what they are doing, usually because they are up to no good.

In such situations, the way in which the data is collected, and then analyzed, and then the decisions that are taken as a result have to be rethought to take account of the adversarial nature of the situation.

I’m interested in how to do knowledge discovery in these adversarial situations, and this blog will talk about the issues, the techologies, and some of the known results.

Adversarial situations include:

  • crime;
  • fraud (medical, insurance);
  • money laundering;
  • organizational malfeasance;
  • industrial espionage;
  • national defence; and
  • counterterrorism.

What bad guys do in these situations has huge costs. The cost of terorrism is obvious, but it’s less well-known that fraud costs an estimated 12% of GDP in developed economies.

Of course, the process of collecting and analyzing data is not necessarily benign, and many people have privacy concerns. We’ll talk about them too.



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