Briefs

Privacy, efficiency on the agenda

Kim Taipale, founder and director of the Center for Advanced Studies in Science and Technology Policy in New York, takes an approach to data mining that melds his training as an attorney with evaluation of the uses of modeling and statistics.

Taipale, who is a partner and adviser to investment companies and banks, also serves on the board of the Markle Task Force on National Security in the Information Age and the steering committee of the American Law Institute�s project on government access to personal data.

He testified earlier this month before the Senate Judiciary Committee about how federal agencies could use advanced analytic tools to protect privacy and improve investigative methods.

GCN: What types of data analysis would be most likely to pinpoint links to social networks of criminal gangs or terrorist networks?

TAIPALE: I am not sure that data mining should be thought of solely in regard to the question of detecting terrorists. I think [advanced analytic software] can be used effectively to allocate intelligence resources to more productive uses. It means you are not using automated means to cast suspicion on anyone in particular, but rather to shift intelligence and law enforcement resources to more effective uses.

For example, it is common for big-city police forces to use Compstat or similar statistically based policing tools to allocate resources to high-crime areas. When you do that, you are not saying that any particular person in that neighborhood is a suspect, you are targeting resources more effectively. In counterterrorism, it is the same construct, of deploying resources on a risk and threat management basis.

The goal is to use statistical models to shift resources depending on the particular [events, individuals, networks or incidents] you are looking for.

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