Social Network Inference and Privacy Preserving Trajectory Publishing in Mobile Phone Data

MPhil Thesis Defence


Title: "Social Network Inference and Privacy Preserving Trajectory Publishing in
Mobile Phone Data"

By

Mr. Maocheng Li


Abstract

Mobile phone data are collected communication logs between human beings. 
There are two interesting aspects and applications of the data: finding 
social structures and mobility patterns. The data could not only offer 
insights about how people make friends with each other but also shed light 
on how people move around in cities. These questions could help potential 
applications in security control and smart city planning.

One interesting problem in social networks is Social Network Inference 
problem. Given the original raw communication data, how to accurately 
infer the relevant social network from the raw data? Are there noisy 
actors to affect the legitimacy of the social network? We consider the 
noise removal process as an important issue in Social Network Inference 
process. In this work, the noise removal problem is formulated and 
studied. Effective noise removing techniques are proposed to tackle the 
problem.

Another important application of the data is about the whereabouts of 
human beings. However, the privacy issue is prohibiting the sharing and 
study of the data. Recent study shows that more than 50% of the population 
in the United States could be uniquely identified if a similar mobile 
phone data as ours is published even with anonymization on the IDs. We 
formulate an attack called top location attack and prove that it is an 
NP-Complete problem to prevent such attack. Then, we propose our novel 
privacy preserving technique to modify the original data with minimal 
distortion.


Date:			Monday, 4 June 2012

Time:			9:30am – 11:30am

Venue:			Room 3501
 			Lifts 25/26

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Dr. Lei Chen (Chairperson)
 			Dr. Raymond Wong



**** ALL are Welcome ****