e-mail me

Jialiu Lin 林佳骝

School of Computer Science
Computer Science Department
Carnegie Mellon University

Office: Wean Hall 4121  


Mobile: +1-412-330-9998  
SocNets: Facebook  

I am a Ph.D student of Computer Science Department at Carnegie Mellon University since Aug 2007 and currently co-advised by Prof. Norman M. Sadeh and Prof. Jason I. Hong. My expected graduation date will be in Dec, 2013.

Before that, I got my bachelor degree at the Hong Kong University of Science and Technology (HKUST) in June 2007 (Computer Engineering Program -- CPEG) with the Academic Achievement Award. At there, I worked with Prof. Lional M. Ni on wireless sensor networks for my undergraduate final year thesis.

My curent research interests focus on mobile systems and applications, especially the location based services and the privacy issues involved in the mobile computing.

|Proposal Abstract|Proposal Full Doc|Presentation Slides|

Abstract A number of ongoing research efforts focus on protecting mobile users’ privacy and security, using software analysis techniques or security extensions with app-specific privacy controls. These proposed extensions might overwhelm users with unnecessary and difficult to understand details.  Unfortunately, there has been little work done to understand users’ privacy preferences regarding mobile apps.  A key question is whether it is possible to identify how apps' privacy-related behaviors impact users' privacy preferences in order to simplify the decisions users have to make without reducing their level of control over the decisions they really care about.
The proposed dissertation work aims to help answer this question. Specifically, we propose to use crowdsourcing and user-oriented machine learning techniques to capture and quantitatively model users' privacy preferences regarding mobile apps. We will perform detailed static analysis on a representative set of apps on the Android platform to understand their private resource usages. We will also use crowdsourcing to collect users' perceptions of these apps, including their expectations and levels of comfort in using these apps. The idea is to identify a relatively small number of sensitive data usage scenarios that most significantly impact users’ privacy decisions when using a particular mobile app. By performing clustering, we expect to isolate different classes of mobile apps that elicit common privacy concerns and different groups of users with distinct privacy preferences. Based on these clusters, we want to see if we can identify a small number of user-understandable privacy profiles (or “personas”) that can be used to simplify the privacy settings users could be exposed to.
The findings of this thesis can offer insight into improving current mobile privacy interfaces and settings. As a by-product, our resulting models and findings could also help mobile app developers estimate the user acceptance of their apps from a privacy perspective.

Thesis Committee
Prof. Norman Sadeh (co-chair), School of Computer Science, CMU
Prof. Jason I. Hong (co-chair), Human-Computer Interaction Institute, CMU
Prof. Mahadev Satyanarayanan, Computer Science Department, CMU
Dr. Sunny Consolvo, Google, Inc.


|Home| |Resume| |Projects| |Hobbies|