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Sezaki Laboratory (University of Tokyo, Japan)


The current research topics of our laboratory are as follow.

Interface for Defining the Privacy of Sensor Data based on Users’ Preferences

Definition of the privacy keeps changing…
The definition of the ‘Privacy’ is constantly affected by
- the social situations
- common sense
- the privacy safeguards
- accessible computer technologies

HCI/social science/public policy researchers investigate general users’ preferences with questionnaires
- high cost
- low reliability
- temporal data

more detail... [pdf: 592KB]

Estimation of Crowd Density and Mobility in Mass Event Using Wi-Fi Direct

Human mobility behaviors emerged in social events involving huge masses of individuals bears potential hazards for irrational social densities.
This year, 36 people died during the “New Year’s Eve stampede” in Shanghai.
Therefore,we need an effective way to monitor crowd density and mobility in mass event. Real-time information and communication are key factors in preventing crowd disasters.

more detail... [pdf: 868KB]

Secure Transmission through Multihop Relaying in Wireless Body Area Networks

How to enhance physical layer security?
Increase the channel capacity of the legitimate link and (or) reduce that of the wiretap link
Motivation of our work: multihop relaying can increase the channel quality of the legitimate link effectively in WBAN due to the sever path loss caused by the human body.

more detail... [pdf: 418KB]

MoveSense: A spatio-temporal Clustering Technique for Discovering Residence Change in Mobile Phone Data

In this work we investigate two research questions. First, whether we can discover a persons’ residence change from unlabeled phone data.
Secondly, if we can develop an algorithm that can automatically carry out this task. To this end, we first formulate what we call the residence change discovery problem. Next, we propose a sequential spatio-temporal clustering technique-MoveSense to solve this problem. We then conduct experiments to validate our technique and find that across the three categories of test datasets, our technique performed well with average detection rate of 71 percent, 68 percent and 72 percent.

more detail... [pdf: 974KB]

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