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SUMMARY:Promoting location privacy... one lie at a time - Daniele Quercia 
 (University of Cambridge)
DTSTART:20110303T160000Z
DTEND:20110303T170000Z
UID:TALK29306@talks.cam.ac.uk
CONTACT:Eiko Yoneki
DESCRIPTION:Nowadays companies increasingly aggregate location data from d
 ifferent sources on the Internet to offer location-based services such as 
 estimating current road traffic conditions\, and finding the best nightlif
 e locations in a city. However\, these services have also caused outcries 
 over privacy issues. As the volume of location data being aggregated  expa
 nds\, the comfort of sharing one's whereabouts with the public at large wi
 ll unavoidably decrease. Existing ways of aggregating location data in the
  privacy literature are largely centralized in that they rely on a trusted
  location-based service.\nInstead\, we propose a piece of software (SpotME
 ) that can run on a mobile phone and allows privacy-conscious users of loc
 ation-based services to report\, in addition to their actual locations\, a
 lso some erroneous locations. The erroneous locations are selected by a ra
 ndomized response algorithm in a way that makes it possible to accurately 
 collect and process aggregated location data without affecting the fidelit
 y of the result. We evaluate the accuracy of SpotME in estimating the numb
 er of people in a certain location upon two very different realistic mobil
 ity traces: the mobility of vehicles in urban\, suburban and rural areas\,
   and the mobility of subway train passengers in Greater London.  We find 
 that erroneous locations have little effect on the estimations (in both tr
 aces\, the error is below 18% for a situation in which more than 99% of th
 e locations are erroneous)\, yet they guarantee that users cannot be local
 ized with high probability. Also\, the computational and storage overheads
  for a mobile phone running SpotME are negligible\, and the communication 
 overhead is limited (SpotME adds an overhead of 21 byte/s).\n
LOCATION:SS03 of the Computer Lab
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