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SUMMARY:Quantifying Privacy Loss of Human Mobility Graph Topology - Dionys
 is Manousakas (Computer Lab)
DTSTART:20180503T140000Z
DTEND:20180503T150000Z
UID:TALK102538@talks.cam.ac.uk
CONTACT:Liang Wang
DESCRIPTION:Abstract: Human mobility is often represented as a mobility ne
 twork\, or graph\, with nodes representing places of significance which an
  individual visits\, such as their home\, work\, places of social amenity\
 , etc.\, and edge weights corresponding to probability estimates of moveme
 nts between these places. Previous research has shown that individuals can
  be identified by a small number of geolocated nodes in their mobility net
 work\, rendering mobility trace anonymization a hard task. In this paper w
 e build on prior work and demonstrate that even when all location and time
 stamp information is removed from nodes\, the graph topology of an individ
 ual mobility network itself is often uniquely identifying. Further\, we ob
 serve that a mobility network is often unique\, even when only a small num
 ber of the most popular nodes and edges are considered. We evaluate our ap
 proach using a large dataset of cell-tower location traces from 1500 smart
 phone handsets with a mean duration of 430 days. We process the data to de
 rive the top−N places visited by the device in the trace\, and find that
  93% of traces have a unique top−10 mobility network\, and all traces ar
 e unique when considering top−15 mobility networks. Since mobility patte
 rns\, and therefore mobility networks for an individual\, vary over time\,
  we use graph kernel distance functions\, to determine whether two mobilit
 y networks\, taken at different points in time\, represent the same indivi
 dual. We then show that our distance metrics\, while imperfect predictors\
 , perform significantly better than a random strategy and therefore our ap
 proach represents a significant loss in privacy.\n\n(work with Cecilia Mas
 colo\, Alastair R. Beresford\, Dennis Chan\, and Nikhil Sharma)\nhttps://p
 etsymposium.org/2018/files/papers/issue3/popets-2018-0018.pdf\n\nBio: Dion
 ysis is a second year PhD student at the Computer Laboratory\, University 
 of Cambridge\, advised by Prof. Cecilia Mascolo. He holds a 5-years Diplom
 a in Electrical & Computer Engineering from the National Technical Univers
 ity of Athens and an MSc in Machine Learning from University College Londo
 n. Prior to moving to Cambridge\, he spent a year in the industry working 
 as a data scientist. He is recipient of a scholarship by Nokia Bell Labs.\
 n
LOCATION:FW26\, Computer Laboratory\, William Gates Building
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