Injecting discrimination and privacy awareness into pattern discovery (Contributo in atti di convegno)

Type
Label
  • Injecting discrimination and privacy awareness into pattern discovery (Contributo in atti di convegno) (literal)
Anno
  • 2012-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/ICDMW.2012.51 (literal)
Alternative label
  • Hajian S., Monreale A., Pedreschi D., Domingo F.J., Giannotti F. (2012)
    Injecting discrimination and privacy awareness into pattern discovery
    in IEEE, 12th International Conference on Data Mining Workshops, ICDMW 2012., Brussels, 10 December 2012
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Hajian S., Monreale A., Pedreschi D., Domingo F.J., Giannotti F. (literal)
Pagina inizio
  • 360 (literal)
Pagina fine
  • 369 (literal)
Note
  • PuMa (literal)
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Universitat Rovira i Virgili, Tarragona, Catalonia; Computer Science Department, University of Pisa; Computer Science Department, University of Pisa; Universitat Rovira i Virgili, Tarragona, Catalonia; CNR-ISTI, Pisa (literal)
Titolo
  • Injecting discrimination and privacy awareness into pattern discovery (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 978-1-4673-5164-5 (literal)
Abstract
  • Data mining is gaining societal momentum due to the ever increasing availability of large amounts of human data, easily collected by a variety of sensing technologies. Data mining comes with unprecedented opportunities and risks: a deeper understanding of human behavior and how our society works is darkened by a greater chance of privacy intrusion and unfair discrimination based on the extracted patterns and profiles. Although methods independently addressing privacy or discrimination in data mining have been proposed in the literature, in this context we argue that privacy and discrimination risks should be tackled together, and we present a methodology for doing so while publishing frequent pattern mining results. We describe a combined pattern sanitization framework that yields both privacy and discrimination-protected patterns, while introducing reasonable (controlled) pattern distortion. (literal)
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