[Colloq] Thesis Proposal - Yabing Liu - Privacy in Online Social Networks: Measurement, Analysis, and Implications - Wed, July 15th, 1pm, 366 WVH

[Fong, Andy]

Privacy in Online Social Networks: Measurement, Analysis, and Implications

Abstract:
The sharing of personal data has emerged as a popular activity over online social networking services (OSNs) such as Facebook. Users today access these services via both web sites and mobile applications (apps). OSNs have access to all user data of millions of users-including uploaded content, personal information (PI) and activities-and they monetize this data by selling it to third parties via their site- specific advertising services. The OSN-based ad services have become popular because third parties (e.g., advertisers) can target users directly (by taking advantage of user-provided data). As a result, user privacy has become incredibly important, however, the OSN business model is often opposed to user privacy in these sites. Unfortunately, the research community is limited by the fact that the full extent of the privacy problem remains unknown; there is little quantification of privacy settings, the difficulty users face when managing their privacy, or the scope, characteristics, and operation of the OSN ad markets that provide the economic basis for OSN services.

In this thesis proposal, I aim to better understand both sides of the underlying privacy problem in the OSN ecosystem: from the user's perspective and from the third party's perspective. I first examine the user's perspective by measuring the disparity between the desired and actual privacy settings, to quantify the magnitude of the problem users have when managing privacy. Then based on my findings, I explore the potential to improve privacy controls. Specifically, I develop Friendlist Manager, a Facebook application that reduces the user burden in automatically creating and maintaining their friendlists, which can be used as basis for data sharing. Second, I turn to the third party's perspective by examining the OSN's underlying ad market. I focus on the suggested bid feature provided by OSNs, and demonstrate how this feature can be used to explore the relative value of different user demographics and the overall stability of the ad market. Third, I propose to develop mechanisms in automatically detecting how user PI is transmitted in Internet traffic to OSNs and third parties. Overall, the goal of this work is to develop new methodologies to measure and analyze the user privacy problem, develop new privacy-preserving tools, bring visibility to OSN ad markets, and automatically detect when user PI is transmitted in network traffic.

Committee:

Prof. David Choffnes
Prof. Alan Mislove (advisor)
Han Hee Song (external examiner, Cisco systems, Inc.)
Prof. Christo Wilson


Andrew W. Fong
Assistant Director for Graduate Admissions and Enrollment

Northeastern University
College of Computer and Information Science
360 Huntington Avenue
451 West Village H
Boston, MA 02115
617-373-8493
a.fong at neu.edu

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