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SUMMARY:A new relaxation of differential privacy - A part of Women in Data
  Science (WiDS) - Christine O'Keefe (CSIRO Mathematics\, Informatics and S
 tatistics)
DTSTART:20161207T141500Z
DTEND:20161207T150000Z
UID:TALK69370@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:Co-author: Anne-Sophie Charest&nbsp\;<br><br>Agencies and orga
 nisations around the world are increasingly seeking to realise the value e
 mbodied in their growing data holdings\, including by making data availabl
 e for research and policy analysis. On the other hand\, access to data mus
 t be provided in a way that protects the privacy of individuals represente
 d in the data. In order to achieve a&nbsp\;justifiable trade-off between t
 hese competing objectives\, appropriate measures of privacy protection and
  data usefulness are needed. &nbsp\;<br><br>In recent years\, the formal d
 ifferential privacy condition has emerged as a verifiable privacy protecti
 on standard. While differential privacy has had a marked impact on theory 
 and literature\, it has had far less impact in practice. Some concerns inc
 lude the possibility that the differential privacy standard is so strong t
 hat statistical outputs are altered to the&nbsp\;point where they are no l
 onger useful. Various relaxations have been proposed to increase the utili
 ty of outputs\, although none has yet achieved widespread adoption. In thi
 s paper we describe a new relaxation of the differential privacy condition
 \, and demonstrate some of its properties.&nbsp\;<br>
LOCATION:Seminar Room 1\, Newton Institute
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