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SUMMARY:Directions in Big Data Anonymisation - Josep Domingo-Ferrer (Unive
 rsitat Rovira i Virgili )
DTSTART:20161205T144000Z
DTEND:20161205T151000Z
UID:TALK69314@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The explosion of big data opens such huge analytical and infer
 ential possibilities that they may allow modeling the world and predicting
  its evolution with great accuracy.  The dark side of such data abundance 
 is that it complicates the preservation of individual privacy: big data la
 rgely feed on the digital trace of our activities. Facing the tension betw
 een big data and privacy\, we find two extreme positions that strive for h
 egemony: on the one side\, the nihilists claim that it is delirious to try
  to maintain one&#39\;s privacy in the big data world\, and that the best 
 we can hope for is that our data are not misused (if this means anything)\
 ; on the other hand\, the fundamentalists propose privacy protection metho
 ds so drastic that their application would destroy nearly all the analytic
 al interest of big data.  We will survey these extreme positions and we wi
 ll describe a midway path\, which we believe more balanced and desirable. 
  This path is based on identifying the utility and privacy requirements of
  big data and trying to satisfy them through an evolution of the statistic
 al disclosure control methods developed in the last 40 years.  We will als
 o briefly touch on transparent\, local and collaborative anonymization as 
 ways to reduce the power of the data controller in front of individual sub
 jects.  <br><br><br><br>
LOCATION:Seminar Room 1\, Newton Institute
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