Distributed and privacy preserving algorithms for mobility information processing
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Katsikouli2018.pdf (10.45Mb)
Date
02/07/2018Item status
Restricted AccessEmbargo end date
31/12/2100Author
Katsikouli, Panagiota
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Abstract
Smart-phones, wearables and mobile devices in general are the sensors of our modern
world. Their sensing capabilities offer the means to analyze and interpret our
behaviour and surroundings. When it comes to human behaviour, perhaps the
most informative feature is our location and mobility habits. Insights from human
mobility are useful in a number of everyday practical applications, such as
the improvement of transportation and road network infrastructure, ride-sharing
services, activity recognition, mobile data pre-fetching, analysis of the social behaviour
of humans, etc.
In this dissertation, we develop algorithms for processing mobility data. The
analysis of mobility data is a non trivial task as it involves managing large quantities
of location information, usually spread out spatially and temporally across
many tracking sensors. An additional challenge in processing mobility information
is to publish the data and the results of its analysis without jeopardizing the
privacy of the involved individuals or the quality of the data.
We look into a series of problems on processing mobility data from individuals
and from a population. Our mission is to design algorithms with provable
properties that allow for the fast and reliable extraction of insights. We present
efficient solutions - in terms of storage and computation requirements - , with a
focus on distributed computation, online processing and privacy preservation.