BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Memento EPFL//
BEGIN:VEVENT
SUMMARY:Depersonalization of Location Traces
DTSTART:20090626T111500
DTSTAMP:20260916T062820Z
UID:8176999a644fb726df49fdfc0b2550f8af8326c583f1937481774a55
CATEGORIES:Conferences - Seminars
DESCRIPTION:Prof. Marco Gruteser\, Rutgers University\, NJ\, USA\nMotivate
 d by a probe-vehicle based automotive traffic monitoring system\, this tal
 k addresses the problem of guaranteeing anonymity in a dataset of location
  traces while maintaining high data accuracy. An analysis of a set of GPS 
 traces from 239 vehicles shows that known privacy algorithms cannot meet a
 pplication accuracy requirements or fail to provide privacy guarantees for
  drivers in low-density areas. To overcome these challenges\, I will prese
 nt a novel time-to-confusion criterion to characterize privacy in a locati
 on dataset and propose a centralized density-aware path cloaking algorithm
  that hides location samples in a dataset to provide a time-to-confusion g
 uarantee for all vehicles. This approach effectively guarantees worst case
  tracking bounds\, while achieving significant data accuracy improvements.
  I will then discuss a distributed scheme building on virtual trip lines\,
  which does not need to rely on a trustworthy privacy server with access t
 o all traces. \nProf. Gruteser's homepage
LOCATION:BC 01 https://plan.epfl.ch/?room==BC%2001
STATUS:CONFIRMED
END:VEVENT
END:VCALENDAR