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Title: Distributed and privacy preserving algorithms for mobility information processing
Author: Katsikouli, Panagiota
ISNI:       0000 0004 7230 4724
Awarding Body: University of Edinburgh
Current Institution: University of Edinburgh
Date of Award: 2018
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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.
Supervisor: Sarkar, Rik ; Marina, Mahesh Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: location data ; mobility data ; analysis ; algorithmic solutions ; sampling frequency ; distributed algorithms