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Resource Abstract:
Distributed data mining from privacy-sensitive multi-party data is likely to play an important role in the next generation of integrated vehicle health monitoring systems. For example, consider an airline manufacturer [tex]$ mathcal{C}$[/tex] manufacturing an aircraft model [tex]$A$[/tex] and selling it to five different airline operating companies [tex]$ mathcal{V}_1 dots mathcal{V}_5$[/tex]. These aircrafts, during their operation, generate huge amount of data. Mining this data can reveal useful information regarding the health and operability of the aircraft which can be useful for disaster management and prediction of efficient operating regimes. Now if the manufacturer [tex]$ mathcal{C}$[/tex] wants to analyze the performance data collected from different aircrafts of model-type [tex]$A$[/tex] belonging to different airlines then central collection of data for subsequent analysis may not be an option. It should be noted that the result of this analysis may be statistically more significant if the data for aircraft model [tex]$A$[/tex] across all companies were available to [tex]$ mathcal{C}$[/tex]. The potential problems arising out of such a data mining scenario are:
Citation
Title Privacy Preserving Distributed Data Mining
revision  Date   2010-09-29T17:13:50
Theme keywords (theme):
dashlink
Ames
NASA
Resource language:  [u'en-US']
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public
point of contact - publisher
individual Name {u'hasEmail': u'mailto:kamalika.das@nasa.gov', u'fn': u'Kamalika Das'}
organisation Name  {u'subOrganizationOf': {u'subOrganizationOf': {u'name': u'U.S. Government'}, u'name': u'National Aeronautics and Space Administration'}, u'name': u'Dashlink'}
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Metadata data stamp:  2010-09-29T17:13:50
Metadata contact - publisher
Metadata scope code  dataset
Metadata standard for this record:  ISO 19115:2003 - Geographic information - Metadata
standard version:  ISO 19115:2003
Metadata record identifier:  DASHLINK_28

Metadata record format is ISO19139 XML (MD_Metadata)