BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Intelligent Location-Privacy Preserving Mechanisms - Reza Shokri\,
  EPFL 
DTSTART:20130411T090000Z
DTEND:20130411T100000Z
UID:TALK43997@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:The widespread use of smart mobile devices has fostered the de
 velopment of a variety of successful data-sharing mobile applications. The
  data that users disclose to untrusted entities (for example\, through loc
 ation-based services) exposes aspects of their private life\, which is not
  apparent at first but can be inferred from the revealed data. People are 
 notoriously bad estimators of risks\, including privacy risks. Moreover\, 
 due to various cognitive biases\, lack of enough information\, and the com
 plexity of the decision problem\, it is difficult for users to make the op
 timal decision about whether to reveal or obfuscate their information and\
 , if necessary\, how to obfuscate it. In this talk\, I address the problem
  of protecting users' privacy in data-sharing mobile applications\, with t
 he focus on location-based services. I propose strategic algorithms and in
 telligent tools to automatically assess the users' location-privacy level\
 , and to find the right balance between revealing and hiding their data. I
  will present the Location-Privacy Meter (LPM) tool\, that we developed to
  quantify location privacy\, as well as our work on optimal defense mechan
 isms against location-inference attacks.
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
END:VEVENT
END:VCALENDAR