BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Data Anonymisation and Quantifying Risk Competition - Hiroaki Kiku
 chi (Meiji University)
DTSTART:20161205T140000Z
DTEND:20161205T142000Z
UID:TALK69312@talks.cam.ac.uk
CONTACT:INI IT
DESCRIPTION:<span><span>One of the main difficulties is to be able to desi
 gn and formalize realistic adversary models\, by taking into account the b
 ackground knowledge of the adversary and his inference capabilities. In pa
 rticular\, many privacy models currently exist in the literature such as <
 i>k</i>-anonymity\, and its extensions such as <i>l</i>-diversity and diff
 erential privacy. However\, these models are not necessarily comparable an
 d what might appear to be the optimal anonymization method in one model is
  not necessarily the best one for a different model. To be able to assess 
 the privacy risks of publishing a particular anonymized data\, it is neces
 sary to evaluate </span>the risk of the data anonymized from a common data
 set.&nbsp\; </span>  &nbsp\;  The main objective of the competition is pre
 cisely to investigate the strengths and limits of existing anonymization m
 ethods\, both from theoretical and practical perspective. More precisely\,
  by given a common dataset containing personal data and history of online 
 retail payments\, some attendances of the competition attempt to anonymize
  the given dataset in a way where re-identification of records of the data
 set is impossible without losing data utility. They are encouraged to try 
 to re-identify the dataset anonymized by the other attendances as well.&nb
 sp\; With pre-defined utility functions and re-identification algorithms\,
  the security and the utility of the anonymized dataset are automatically 
 evaluated as the maximum re-identification probability and the mean averag
 e error between the anonymized data and the original dataset\, respectivel
 y. Throughout the competition\, we aim at gaining an in-depth understandin
 g on how to quantify the privacy level provided by a particular anonymizat
 ion method as well as the achievable trade-off between privacy and utility
  of the resulting data. The outcomes of the meeting will greatly benefit t
 o the privacy community.  <br><br><br><br>
LOCATION:Seminar Room 1\, Newton Institute
END:VEVENT
END:VCALENDAR