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What and Who
Title:Reasoning about trustworthiness of identities in social computing systems
Speaker:Bimal Viswanath
coming from:Max Planck Institute for Software Systems
Speakers Bio:
Event Type:SWS Student Defense Talks - Thesis Proposal
Visibility:SWS
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Level:Public Audience
Language:English
Date, Time and Location
Date:Friday, 16 January 2015
Time:16:00
Duration:60 Minutes
Location:Saarbrücken
Building:E1 5
Room:029
Abstract
The huge popularity of social computing platforms such as Facebook, Google+, Twitter, Yelp and Twitter has attracted rampant forms of service abuse on these platforms. While this degrades a user's experience, service abuse can also have serious economic consequences; click fraud in social ad platforms is a significant concern for advertisers. The main cause for this lack of reliability is the identity infrastructure: in most systems, the users operate behind weak identities that are easy to create. The use of weak identities makes these systems vulnerable to Sybil attacks, where an attacker creates multiple fake (Sybil) identities to manipulate and abuse the system. Thus, the key research challenge is: Given a weak identity-based social computing system, how can we reason about the (un)trustworthiness of identities in the system? In this thesis, I propose various schemes to address this problem.

In this talk, I will first present a high level overview of my thesis research where I will describe the limitations of existing approaches to reason about trust and how my work addresses those limitations. Next, I will present my most recent project "Robust Tamper Detection in Crowd Computations". Popular social computing systems increasingly rely on crowd computing to rate and rank content, users, products and businesses. Today, attackers who create Sybil identities can easily tamper with these computations. Existing defenses that largely focus on detecting individual Sybil identities have a fundamental limitation: Adaptive attackers can create hard-to-detect Sybil identities to tamper arbitrary crowd computations. We propose Stamper, an approach for detecting tampered crowd computations. Stamper gains strength in numbers – we propose statistical analysis techniques that can determine if a large crowd computation has been tampered by Sybils, even when it is fundamentally hard to infer which of the participating identities are Sybil.
Contact
Name(s):Maria-Louise Albrecht
Video Broadcast
Video Broadcast:YesTo Location:Kaiserslautern
To Building:G26To Room:112
Meeting ID:
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  • Maria-Louise Albrecht, 01/14/2015 02:08 PM -- Created document.