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Data fusion

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Definitions Edit

Data fusion

occurs when data from different sources are brought into contact and new facts emerge.[1]
involves the exchange of information from different sources — including law enforcement, public safety, and the private sector — and, with analysis, can result in meaningful and actionable intelligence and information. The fusion process turns this information and intelligence into actionable knowledge.[2]
[is a] multi-level process dealing with the association, correlation, combination of data and information from single and multiple sources to achieve refined position, identify estimates and complete and timely assessments of situations, threats and their significance.[3]

Overview Edit

Data fusion is used in a number of technical domains such as sensor networks, video/image processing, robotics and intelligent systems, and elsewhere.

Individually, each data source may have a specific, limited purpose. Their combination, however, may uncover new meanings. In particular, data fusion can result in the identification of individual people, the creation of profiles of an individual, and the tracking of an individual's activities. More broadly, data analytics discovers patterns and correlations in large corpuses of data, using increasingly powerful statistical algorithms. If those data include personal data, the inferences flowing from data analytics may then be mapped back to inferences, both certain and uncertain, about individuals.

Privacy issues Edit

Because of data fusion, privacy concerns may not necessarily be recognizable in born‐digital data when they are collected. Because of signal‐processing robustness and standardization, the same is true of born‐analog data — even data from a single source (e.g., a single security camera). Born‐digital and born‐analog data can both be combined with data fusion, and new kinds of data can be generated from data analytics. The beneficial uses of near‐ubiquitous data collection are large, and they fuel an increasingly important set of economic activities.

Taken together, these considerations suggest that a policy focus on limiting data collection will not be a broadly applicable or scalable strategy — nor one likely to achieve the right balance between beneficial results and unintended negative consequences (such as inhibiting economic growth).

References Edit

  1. Big Data and Privacy: A Technological Perspective, at x.
  2. Fusion Center Guidelines: Developing and Sharing Information and Intelligence in a New Era, at 2.
  3. U.S. Department of Defense, Joint Director of Laboratories Workshop (JDL Workshop 1991), "Data Fusion Lexicon,” Technical Panel For C3, F.E. White, San Diego, CA: Code 4, cited in Framework for Cyber-Physical Systems, at 70.

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