Events Calendar

PhD Defense - Bin Liu

PhD Candidate: Bin Liu

Title: Improving Efficiency, Privacy and Robustness for Crowd-Sensing Applications

Committee:
Ramesh Govindan (chair)
Leana Golubchik
Sandeep Gupta (outside member)

Abstract:

Every year, a wide variety of modern smart devices, such as smartphones and tablets, are released by big brands, like Apple, Samsung and HTC. Compared to previous generations, these smart devices are more sophisticated in two ways: (a) they run advanced operating systems which allow developers to create a large collection of complicated apps, and (b) they have more diverse sensors which can be used to perform various context-aware tasks. These two attributes, together, have conceived a new class of applications, crowd-sensing. Crowd-sensing is a capability by which a task requestor can recruit smartphone users to provide sensor data to be used towards a specific goal or as part of a social or technical experiment. For the purpose of supporting crowd-sensing tasks, professional apps are developed to provide specialized platforms, and high quality sensors are used to generate semantically rich data.

My dissertation focuses on possible ways to improve efficiency, privacy and robustness for crowd-sensing applications. First, targeting the general form of crowd-sensing, we design efficient algorithms to answer the following question: how to optimize the selection of crowd-sensing participants to deliver credible information about a task? Based on a model about credibility of information, we develop solutions for the discrete version and the time-averaged version of this problem.

Second, we consider a special crowd-sensing case in which Internet-connected mobile users contribute sensor data as training samples, and collaborate on building a model for classification tasks such as activity or context recognition. Constructing the model can naturally be performed by a service running in the cloud, but users may be more inclined to contribute training samples if the privacy of these data could be ensured. For this, we develop algorithms and an associated system design to perform collaborative learning task in a way that preserves user data privacy without significant loss of accuracy.

Finally, the technique of dynamic analysis can be employed to test many aspects of crowd-sensing apps, such as performance, security, and correctness properties. As an initial attempt, we show how to use dynamic analysis to detect placement ad fraud in which app developers manipulate visual layouts of ads in ways that result in invisible ad impressions and accidental clicks from real users. We demonstrate that the detection can be performed using optimized automated navigation methods in a large set of 1,150 tablet apps and 50,000 phone apps.

Location: Henry Salvatori Computer Science Center (SAL) - 222

Audiences: Everyone Is Invited

Contact: Lizsl De Leon