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SUMMARY:Rothschild Lecture: The Promise of Differential Privacy - Cynthia 
 Dwork (Harvard University)
DTSTART:20161130T160000Z
DTEND:20161130T170000Z
UID:TALK69230@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:The rise of "Big Data" has been accompanied by an increase in 
 the twin risks of spurious scientific discovery and privacy compromise.&nb
 sp\; A great deal of effort has been devoted to the former\, from the use 
 of sophisticated validation techniques\, to deep statistical methods for c
 ontrolling the false discovery rate in multiple hypothesis testing.&nbsp\;
  However\, there is a fundamental disconnect between the theoretical resul
 ts and the practice of data analysis: the theory of statistical inference 
 assumes a fixed collection of hypotheses to be tested\, selected non-adapt
 ively before the data are gathered\, whereas in practice data are shared a
 nd reused with hypotheses and new analyses being generated on the basis of
  data exploration and the outcomes of previous analyses. Privacy-preservin
 g data analysis also has a large literature\, spanning several disciplines
 . However\, many attempts have proved problematic either in practice or on
  paper.&nbsp\;   &nbsp\;<br>  "Differential privacy" &ndash\; a recent not
 ion tailored to situations in which data are plentiful &ndash\; has provid
 ed a theoretically sound and powerful framework\, giving rise to an explos
 ion of research. We will review the definition of differential privacy\, d
 escribe some basic algorithmic techniques for achieving it\, and see that 
 it also prevents false discoveries arising from adaptivity in data analysi
 s.  &nbsp\;
LOCATION:Seminar Room 1\, Newton Institute
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