Robust expert ranking in online communities fighting sybil attacks
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8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing October 14–17, 2012 Pittsburgh, Pennsylvania, United States . Robust Expert Ranking in Online Communities - Fighting Sybil Attacks. Khaled A. N. Rashed , Cristina Balasoiu, Ralf Klamma

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Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

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Robust expert ranking in online communities fighting sybil attacks

8th IEEE International Conference on

Collaborative Computing:

Networking, Applications and Worksharing

October 14–17, 2012 Pittsburgh, Pennsylvania, United States

Robust Expert Ranking in Online Communities - Fighting Sybil Attacks

Khaled A. N. Rashed, Cristina Balasoiu, Ralf Klamma

RWTH Aachen UniversityAdvanced Community Information Systems (ACIS)

[email protected]


Robust expert ranking in online communities fighting sybil attacks

Advanced Community Information Systems (ACIS)

Web Analytics

Web Engineering

Requirements Engineering


Agenda

Agenda

  • Introduction and motivation

  • Related work

  • Our Approach

    • Expert ranking algorithm

    • Robustness of the expert ranking algorithm

  • Evaluation

  • Conclusions and outlook


Introduction

Introduction

  • Theexpert search and ranking refer to the way of finding a group of authoritative users with special skills and knowledge for a specific category.

  • The task is very important in online collaborative systems

  • Problems: openness and misbehaviour and

    • No attention has been made to the trust and reputation of experts

  • Solution: Leveraging trust


Motivation examples

Motivation Examples

Manipulating the truth for war propaganda

Tidal bores presented as Indian Ocean Tsunami

  • Published as: 2004 Indian Ocean Tsunami

  • Proved to be tidal bores, a four-day-long government-sponsored tourist festival inChina

  • Published as: British soldiers abusing prisoners in Iraq

  • Proved to be fake by Brigadier Geoff Sheldon who said the vehicle featured in the photo had never been to Iraq

  • Expert knowledge, analysis and witnesses are needed to identify the fake!


A case study collaborative fake multimedia detection system

A Case Study:Collaborative Fake Multimedia Detection System

  • Collaborative activities (rating, tagging and commenting)

    • Provide new means of search, retrieval and media authenticity evaluation

    • Explicit ratings and tags are used for evaluating authenticity of multimedia items

    • Reliability: not all of the submitted ratings are reliable

    • No centralized control mechanism

    • Vulnerability to attacks

  • Three types of users

    • Honest users

    • Experts

    • Malicious users


Robust expert ranking in online communities fighting sybil attacks

Research Questions and Goals

  • Research questions

    • How to measure users’ expertise in collaborative media sharing and evaluating systems? and how to rank them?

    • What is the implication of trust

    • Robustness! how to ensure robustness of the ranking algorithm

  • Goals

    • Improve multimedia evaluation

    • Reduce impacts of malicious users


Related work

Related Work

  • Probabilistic models e.g.[Tu et al.2010]

  • Voting models [Macdonald and Ounis 2006] [Macdonald et al.2008]

  • Link-based approaches PageRank[Brein and Page 1998], HITS[Kleinberg1999] and their variations. SPEAR algorithm[Noll et al. 2009] ExpertRank [Jiao et al. 2009]

  • TREC enterprise track -Find the associations between candidates and documents e.g.[Balog 2006, Balog 2007]

  • Machine learning algorithms e.g. [Bian and Liu 2008, Li et al. 2009]


Our approach

Our Approach

  • Assumptions

    • Expert users tend to have many authenticity ratings

    • Correctly evaluated media are rated by users of high expertise

    • Following expert users provides more benefits

  • Expert definition

    • Rates a big number of media files in an authentic way with respect to a topic and Highly trusted by his directly connected users

    • Should be trustable in evaluating multimedia


Expert ranking methods

Expert Ranking Methods

  • Domain knowledge driven method

    • Considers tags that users assign to media files

    • User profile: merging tags user submitted to the media files in the system

    • Similarity coefficient between the candidate profile and the tags assigned to a specific resource

    • Used to reorder users who voted a media file according to the tag profile

  • Domain knowledge independent method

    • Use the connections between users and resources to decide on the expertise of the users

    • A modified version of HITS algorithm

    • Mutual reinforcement of users expertise and media


Mhits expert ranking algorithm

MHITS : Expert Ranking Algorithm

  • MHITS: Expert ranking algorithm in online collaborative systems

    • Link-based approach, based on HITS algorithm

    • HITS

      • Authorities: pages that are pointed to by good pages

      • Hubs: pages that points to good pages

      • Reinforcement between hubs and authorities

    • MHITS

      • Users act as hubs (correctly evaluated media rated by them)

      • Media files act as authorities

      • Mutual reinforcement between users and media files

      • Local trust values between users are assigned

      • Considers the rates of the users


Mhits expert ranking algorithm1

MHITS: Expert Ranking Algorithm

  • one network for users and ratings

  • one for users only (trust network).

  • Trust in range [0, 1]

  • Ratings 0.5 for a fake vote,

  • 1 for an authentic vote


Robustness of the mhits algorithm

Robustnessofthe MHITS Algorithm

  • Compromising techniques

    • Sybil attack [Douc02], Reputation theft, Whitewashing attack, etc.

    • Compromising the input and the output of the algorithm

  • Sybil attack

    • Fundamental problem in online collaborative systems

    • A malicious user creates many fake accounts (Sybils) which all reference the user to boost his reputation (attacker’s goal is to be higher up in the rankings)

  • Countermeasures against Sybil attack


Sumup

SumUp

  • Centralized approach

    • Aimsto aggregate votes in a Sybil resilient manner

  • Key idea – adaptive vote flow technique - that appropriately assigns and adjusts link capacities in the trust graph to collect the votes for an object

  • New: weIntegrate SumUp with the MHITS Java implementation – used own data structure based on Java Sparse Arrays

  • SumUp Steps

  • Assign the source node and number of votes per media file

  • Levels assignment

  • Pruning step

  • Capacity assignment

  • Max-flow computation – collect votes on each resource

  • Leverage user history to penalize adversarial nodes


Integration of sumup with mhits

Integration of SumUp with MHITS


Evaluation

Evaluation

  • Experimental Setup

    • BarabasiAlbert model for generating network

    • 300 users

    • 20 media files (10 known to be fake and 10 known to be authentic)

    • 800 ratings

    • 3000 trust edges


Ratings distribution

Ratings Distribution


Evaluation1

Evaluation

  • Evaluation metrics:

    • [email protected]

    • Spearman’s rank correlationcoefficient

      p - Spearman’s coefficient of rank correlation -1 ≤ps ≤ 1

      di - is the different between the rank of xi and the rank of yi

      n:- the number of data points in the sample (total number of observations)

    • ps = - 1 or 1 high degree of correlation between x any y

    • Ps = 0 a lack of linear association between two variables

0

-1

+1

Perfect Positive

Correlation

Perfect Negative

Correlation

No Correlation


Experimental results i

Experimental Results I

  • No Sybils

  • Results are compared with the ranking

    • of the users according to the number of

    • fair ratings each of them had in the system


Experimental results ii

Experimental Results II

  • 10% Sybils

  • 4 attack edges


Experimental results iii

Experimental Results III

[email protected]

  • 10% Sybils (one group) and 8 attack edges

  • 20% Sybils (one group) and 24 attack edges


Further evaluation

Further evaluation

  • 3%17% - Number of Sybil votes increased with respect to the total number of fair votes

    • expertise ranking does not change

  • 9 to 14 and 24 Number of attack edges was increased keeping the number of Sybil votes to 17% percent of the number of fair votes and constant number of Sybils (50)

    • precision does not change

  • 17% 50% and then to 100% the number of Sybil votes Increased keeping constant the Nr of attack edges (24) and Sybils Nr.


Conclusions and future work

Conclusions and Future Work

  • Conclusions

    • Proposed an expertise ranking algorithm in collaborative systems (fake multimedia detection systems)

    • Leveraging trust and showed the trust implications

    • Combination of expert ranking and resistant to Sybils algorithms

  • Future Work

    • Applying the algorithm on real data and on different data sets

    • Temporal analysis –time series analysis

    • Integrate the domain knowledge driven method


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