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SUMMARY:Individual Differential Privacy - Josep Domingo-Ferrer (Universita
 t Rovira i Virgili )\; Jordi  Soria-Comas (Universitat Rovira i Virgili )
DTSTART:20161208T101500Z
DTEND:20161208T110000Z
UID:TALK69390@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Differential privacy is well-known because of the strong priva
 cy guarantees it offers: the results of a data analysis must be indistingu
 ishable between data sets that differ in one record. However\, the use of 
 differential privacy may limit the accuracy of the results significantly. 
 Essentially\, we are limited to data analyses with small global sensitivit
 y (although some workarounds have been proposed that improve the accuracy 
 of the results when the local sensitivity is small). We introduce individu
 al differential privacy (iDP)\, a relaxation of differential privacy that:
  (i) preserves the strong privacy guarantees that differential privacy giv
 es to individuals\, and (ii) improves the accuracy of the results signific
 antly. The improvement in the accuracy comes from the fact that the truste
 d party does a more precise assessment of the risk associated to a given d
 ata analysis. This is possible because we allow the trusted party to take 
 advantage of all the available information\, namely the actual data set.
LOCATION:Seminar Room 1\, Newton Institute
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