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A Policy Forum on the Use of Big Data in Homeland Security

On October 30, 2013, the Bipartisan Policy Center and Intel Corporation hosted a forum to explore the potential for big data innovation to improve homeland security, current and future challenges to overcome, and policy principles that will encourage innovation while safeguarding privacy and security in our increasingly connected society. This event is part of the ongoing Innovation Economy conversation convened by Intel in 2009, focused on the vital role of innovation in sustaining and building upon U.S. competitiveness in the global economy.

Overarching Themes

  • Security and privacy are not mutually exclusive. Using data in effective ways to create a more secure world shouldn’t require a sacrifice of privacy. It does require increased oversight, control, auditing, and accountability programs to ensure that privacy is adequately protected.
  • The definition of data that is relevant to security can be quite broad. Would-be terrorists engage in many familiar daily-life activities?from paying highway tolls to talking on the phone. It is increasingly difficult to define data that is not potentially relevant to terrorist activity.
  • Governing data use is a key challenge. Fair Information Practice Principles and the Privacy Act say that entities that collect data must explain publicly how they intend to use the data?and then must use it only for purposes that are compatible with that intention. The potential for secondary use of data after it is collected is a concern for privacy advocates and suggests a need for careful governance of the entire life cycle of data.
  • Borders are more than lines on a map. Borders represent massive flows of goods and people, and are no longer viewed as the first line of homeland defense, but rather, as the last. The challenge is to push the work of securing the borders outward to the point where flows of goods and people toward the United States originate. Big data is essential in this work, as passenger or freight manifests are cross-checked against databases of known and suspected risks to identify threats as early as possible.

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