Motivation

Content sharing sites allow users to find and share content.

Examples: news articles (Digg), videos (YouTube), URLs.

System can be gamed by to manipulate content rating:

    Sybil attacks: vote multiple times with multiple accounts.

    Buying votes: from legitimate users by offering compensation

But, accounts are often not verified and free to create. Usually only requires email address + CAPTCHA (Multiple identities referred to as Sybils [IPTPS’02]).

Goal and assumptions

Goal:  Create system to bound the effect of creating Sybils or buying ratings

                Key idea:  Leverage underlying social network and use relative ratings

                Social network often already exists on these sites

Design

Our approach is to assign weights to votes and use relative ratings; not all votes are counted equally!

Goal: Assign weights so that user's aggregate weight does not depend on number of identities they possess.

Naturally mitigates the effect of Sybils.

Challenge is choosing weight assignment algorithm

We use flow over the social network to assign weights

1. •    Every voter is a source; each link has unit capacity
   

2. •    User asking for rating is the sink (collector)
   

Relative rating

Defend against vote-buying attacks

Otherwise legitimate users paid small

           compensation of rating.

Convert raw rating to relative rating

Measure item against all other items the user

          rated, ranges between 0 and 1.

Most users provide few ratings

Their relative rating doesn’t provide much    

          benefit.

Related work

DSybil [oakland’09] finds trusted guides (users who have a similar voting history)

1. •    Assumes all users provide enough feedback to find guides
   

2. •    Many users don't vote/feedback in practice
   

SumUp [nsdi’09] uses social network; inspired our design

1. •    Defines a trusted "envelope", where all votes are counted
   

2. •    Nodes outside must compete