Practical Mobile Sensing Applications with Privacy Protection

The Hong Kong University of Science and Technology
Department of Computer Science and Engineering


PhD Thesis Defence


Title: "Practical Mobile Sensing Applications with Privacy Protection"

By

Mr. Shanfeng ZHANG


Abstract

Mobile sensing is becoming an important part of people’s daily life. Thanks to 
various sensors equipped on devices such as smartphones and wearable devices, a 
wide range of human behaviour and context information can be obtained in real 
time. These information can be further used to support a large number of mobile 
applications which bring great convenience to people’s life, such as vehicle 
navigation, location-based advertising and taxi-hailing. However, some topics 
remain hard to be solved. In this thesis, we first propose a mobile sensing 
application : urban taxi-sharing. Then we address two challenging problems for 
practical mobile applications: privacy leakage and cold-start problem.

In the first work, we design a QoS-aware taxi-sharing system named QA-Share. 
QA-Share allows occupied taxis to pick up new passengers on the fly, promising 
to reduce waiting time for taxi riders and increase productivity for drivers. 
Taxi-sharing can also bring significant social and environmental benefits, such 
as relieving traffic jams and saving energy consumption. We address two 
important challenges. First, QA-Share aims to maximize driver profit and user 
experience at the same time. Second, QA-Share continuously optimizes these two 
metrics by dynamically adapting its schedule as new requests arrive, without 
entering an oscillation state.

Most mobile sensing applications rely on the sensing information shared by the 
crowd. Meanwhile, it has raised concerns for location privacy. Users may wish 
to prevent privacy leak and publish as many non-sensitive contexts as possible 
for better user experience of the application. Simply suppressing sensitive 
contexts is vulnerable to the adversaries exploiting spatio-temporal 
correlations in users’ behaviour. In the second work, we present a novel data 
sharing scheme PLP, which preserves privacy while maximizes the amount of data 
collection by filtering a user’s context stream. The experimental results show 
that PLP efficiently protects privacy without sacrificing much utility.

Many context sensing methods are achieved by learning the relationship between 
context information and original sensing data of smartphones. However, manually 
labeling samples collected from smartphone users is time-consuming, 
labor-intensive and money-consuming. It’s hard to collect enough labeled 
samples at first. This is also known as the cold-start problem. In the third 
work, we propose iSelf, which can predict context information in cold-start 
conditions with smartphones. We take emotion detection as an example and show 
that our method can achieve high accuracy with only a few labeled samples.


Date:  			Friday, 11 September 2015

Time:			9:00am - 11:00am

Venue:			Room 3007
 			Lift 4

Chairman:		Prof. Xiaonong Zhu (HUMA)

Committee Members:	Prof. Lionel Ni (Supervisor)
 			Prof. Huamin Qu
 			Prof. Ke Yi
 			Prof. Zongjin Li (CIVL)
 			Prof. Jianping Wang (Comp. Sci., CityU)


**** ALL are Welcome ****