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Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR-0312-1 Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes.

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Präsentation zum Thema: "Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR-0312-1 Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes."—  Präsentation transkript:

1 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Robust Expert Ranking in Online Communities - Fighting Sybil Attacks Khaled A. N. Rashed, Cristina Balasoiu, Ralf Klamma RWTH Aachen University Advanced Community Information Systems (ACIS) 8th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing October 14–17, 2012 Pittsburgh, Pennsylvania, United States

2 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Responsive Open Community Information Systems Community Visualization and Simulation Community Analytics Community Support Web Analytics Web Engineering Advanced Community Information Systems (ACIS) Requirements Engineering

3 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Agenda n Introduction and motivation n Related work n Our Approach –Expert ranking algorithm –Robustness of the expert ranking algorithm n Evaluation n Conclusions and outlook

4 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Introduction n The expert search and ranking refer to the way of finding a group of authoritative users with special skills and knowledge for a specific category. n The task is very important in online collaborative systems n Problems: openness and misbehaviour and – No attention has been made to the trust and reputation of experts n Solution: Leveraging trust

5 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Motivation Examples Manipulating the truth for war propaganda Published as: 2004 Indian Ocean Tsunami Proved to be tidal bores, a four-day-long government-sponsored tourist festival in China Tidal bores presented as Indian Ocean Tsunami Expert knowledge, analysis and witnesses are needed to identify the fake! 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

6 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 A Case Study: Collaborative Fake Multimedia Detection System n 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 n Three types of users –Honest users –Experts –Malicious users

7 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Research Questions and Goals n 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 n Goals –I mprove multimedia evaluation –Reduce impacts of malicious users

8 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Related Work n Probabilistic models e.g.[Tu et al.2010] n Voting models [Macdonald and Ounis 2006] [Macdonald et al.2008] n Link-based approaches PageRank [Brein and Page 1998], HITS [Kleinberg1999] and their variations. SPEAR algorithm [Noll et al. 2009] ExpertRank [Jiao et al. 2009] n TREC enterprise track - Find the associations between candidates and documents e.g.[Balog 2006, Balog 2007] n Machine learning algorithms e.g. [Bian and Liu 2008, Li et al. 2009]

9 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Our Approach n 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 n 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

10 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Expert Ranking Methods n 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 n 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

11 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 MHITS : Expert Ranking Algorithm n 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 – Us ers 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

12 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 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

13 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Robustness of the MHITS Algorithm n Compromising techniques –Sybil attack [Douc02], Reputation theft, Whitewashing attack, etc. –Compromising the input and the output of the algorithm n 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 (attackers goal is to be higher up in the rankings ) n Countermeasures against Sybil attack SybilGuard [YKGF06] SybilLimit [YGKX08] SumUp [TMLS09] Protocol typeDecentralized Centralized Accepted Sybils per attack edge

14 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 SumUp n Centralized approach –Aims to aggregate votes in a Sybil resilient manner n 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: we Integrate SumUp with the MHITS Java implementation – used own data structure based on Java Sparse Arrays SumUp Steps (1) Assign the source node and number of votes per media file (2) Levels assignment (3) Pruning step (4) Capacity assignment (5) Max-flow computation – collect votes on each resource (6) Leverage user history to penalize adversarial nodes SumUp Steps (1) Assign the source node and number of votes per media file (2) Levels assignment (3) Pruning step (4) Capacity assignment (5) Max-flow computation – collect votes on each resource (6) Leverage user history to penalize adversarial nodes

15 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Integration of SumUp with MHITS

16 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Evaluation n 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

17 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Ratings Distribution

18 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Evaluation n Evaluation metrics: –Spearmans rank correlation coefficient p - Spearmans coefficient of rank correlation -1 p s 1 d i - is the different between the rank of x i and the rank of y i n :- the number of data points in the sample (total number of observations) p s = - 1 or 1 high degree of correlation between x any y P s = 0 a lack of linear association between two variables

19 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 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 HITSMHITS Spearman n=

20 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Experimental Results II 1 0% Sybils 4 attack edges HITSMHITSMHITS & SumUp Spearman n=

21 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Experimental Results III 10% Sybils (one group) and 8 attack edges 20% Sybils (one group) and 24 attack edges

22 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Further evaluation n 3% 17% - Number of Sybil votes increased with respect to the total number of fair votes –expertise ranking does not change n 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 n 17% 50% and then to 100% the number of Sybil votes Increased keeping constant the Nr of attack edges (24) and Sybils Nr. K MHITS 20% MHITS & SumUp 20% MHITS 50% MHITS&SumUp 50% MHITS 100% MHITS & SumUp 100%

23 Lehrstuhl Informatik 5 (Information Systems) Prof. Dr. M. Jarke I5-DR Khaled Rashed Cristina Balasoiu Ralf Klamma Deutschen Akademischen Austauschdienstes CollaborateCom2012 Conclusions and Future Work n 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 n 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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