Iolaus: Securing Online Content Rating Systems
- Technology Benefits
- The system:ΓÇóIs highly efficient and scalableΓÇóLeverages the existing network of rating systems to defend against potential attacksΓÇóWeighs each identity rating, allowing different identities to have different weights, enabling effective defense against multiple identity attacksΓÇóAllows for relative ratings to overcome the effect of bought ratingsΓÇóAllows for a personalized aggregated rating for each identity as compared to single aggregated ratings used conventionallyΓÇóFurther allows for an influence bounding, such that total influence per vote is uniquely boundedΓÇóWould be commercially useful for various online content rating sites such as sites sharing business recommendations (Yelp, Trip Advisor), news articles (Digg, reddit), and multimedia content (Flickr, YouTube), in order to manage potential rating manipulations
- Detailed Technology Description
- None
- *Abstract
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Conventional content rating schemes provide a single, global, aggregated rating for each content piece. Moreover, such approaches use a binary choice to accept or reject each identity rating during aggregation. In addition to this, existing approaches view ratings on absolute terms, and also none of the existing techniques provide defense against vote-buying attacks. This invention discloses a novel system for securing online content ratings and related systems, overcoming the existing limitations.
- *Principal Investigator
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Name: Alan Mislove
Department:
Name: Arash Molavi Kakhki
Department:
- Country/Region
- USA
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