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ARM'08 - Keynote Talk

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Invited Keynote Talk @ the 7th Workshop on Adaptive and Reflective Middleware (ARM'08). Co-located with Middleware 2008. December 1st 2008, Leuven, Belgium.

Invited Keynote Talk @ the 7th Workshop on Adaptive and Reflective Middleware (ARM'08). Co-located with Middleware 2008. December 1st 2008, Leuven, Belgium.

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ARM'08 - Keynote Talk ARM'08 - Keynote Talk Presentation Transcript

  • Trust in Pervasive Social Computing Licia Capra Dept of Computer Science University College London
  • EVOLUTION OF MOBILE TECHNOLOGY
  • TRANSFORMATION OF INTERNET USERS View slide
  • A DIGITAL TAPESTRY View slide
  • PERVASIVE SOCIAL COMPUTING
  • A DIGITAL TAPESTRY
    • Millions of different resources available to consumers
    • Content overload
  • A DIGITAL TAPESTRY
    • Users need help to find interesting content
    • An enormous market for niche content is appearing
  • CONNECTING SYPPLY & DEMAND
    • Filters
    • are essential
    • to connect
    • supply
    • and
    • demand
  • COLLABORATIVE FILTERING
      • Quantify user similarity
      • Similar users are deemed competent
      • Limitations
      • Create recommendations based on behaviour of similar users
        • Mass market
        • Subject to abuse
  • SOCIAL NETWORKS
      • Limitations
        • No subjectivity
        • No transitivity
      • Determine user friendship
      • Friends are deemed to have good intent
      • Share content across friendship relations
  • BROWSING THE TAPESTRY Joint work with Matteo Dell’Amico @ Eurecom Presented at the Joint iTrust and PST Conferences on Privacy, Trust Management and Security, June 08
  • BROWSING THE TAPESTRY – OUTLINE
    • Philosophy of the Approach
      • Intent
      • Competence
      • Social Filtering
    • Realisation of the Approach
      • Intent: Personalised PageRank
      • Competence: HITS
      • Social Filtering: SOFIA
    • Experiments
      • Accuracy
      • Robustness
  • BROWSING THE TAPESTRY – OUTLINE
    • Philosophy of the Approach
      • Intent
      • Competence
      • Social Filtering
    • Realisation of the Approach
      • Intent: Personalised PageRank
      • Competence: HITS
      • Social Filtering: SOFIA
    • Experiments
      • Accuracy
      • Robustness
  • PHILOSOPHY OF THE APPROACH
    • Intent
  • PHILOSOPHY OF THE APPROACH
    • Intent: willingness to provide honest judgements
    A B C D Direct Trust Inferred Trust
      • Web of Trust: social network where A is connected to B if A trusts that B behaves honestly
      • Transitivity Pattern: propagate trust over intent (e.g., “I trust the friends of my friends”)
      • Limitation: transitivity does not cater for the taste of users (i.e., subjectivity in niche markets)
  • PHILOSOPHY OF THE APPROACH
    • Competence
  • PHILOSOPHY OF THE APPROACH
    • Competence: ability to provide correct judgements
    A B X Y Direct Trust Inferred Trust
      • Bipartite graph: network of judgements where user A is connected to judgement X if A expressed X
      • Co-citation Pattern: propagate trust over competence (e.g., “I agree with judgements of competent users”)
      • Limitation: co-citation subject to abuse (i.e., Sybil attack, profile injection, shilling, etc.)
  • = Intent & Competence
  • PHILOSOPHY OF THE APPROACH
    • Social Filtering Pattern
      • A can infer trust for judgement Y expressed by D if
        • There exists a directed path from A to D in the web of trust
          • D is trusted to be well intentioned (robustness)
        • A and expressed at least one common judgement
          • D is trusted to be competent (accuracy)
    A D X Y Direct Trust for Judgements Direct Trust for Users C B Inferred Trust for Judgements
  • BROWSING THE TAPESTRY – OUTLINE
    • Philosophy of the Approach
      • Intent
      • Competence
      • Social Filtering
    • Realisation of the Approach
      • Intent: Personalised PageRank
      • Competence: HITS
      • Social Filtering: SOFIA
    • Experiments
      • Accuracy
      • Robustness
  • REALISATION OF THE APPROACH
    • Evaluating Intent
      • Desirable properties
        • Longer paths disperse trust
        • Adding paths increase trust
        • Limit amount of trust propagated through attack edges
    X A B C D
      • PageRank
        • Goal: rank importance of web pages
        • Intuition: an authoritative page is linked by many authoritative pages
      • Swap WWW graph with Web of Trust
        • Goal: rank trustworthiness of users
        • Intuition: an honest user is being trusted by many honest users
    Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. “ The PageRank Citation Ranking: Bringing Order to the Web”. Stanford Digital Library. 1998
  • REALISATION OF THE APPROACH
    • Evaluating Intent
      • Personalised PageRank
        • Start random walk at (honest) evaluating node A
        • Jump from X to Y with probability proportional to edge weight
        • Stop with probability 1- 
      • Importance of 
        • Max rank for Sybil region: p /(1-  )
        • Low  implies shorter paths, higher robustness, faster convergence but we don’t trust honest users when they are socially far away
  • REALISATION OF THE APPROACH
    • Evaluating Competence
      • HITS
        • Web pages are seen as hubs (pages that link to relevant documents, i.e., authorities) and authorities (pages whose content satisfy a query)
        • Intuition: good hubs points to good authorities, and good authorities are pointed to by good hubs
    J. M. Kleinberg. “Authoritative Sources in a Hyperlinked Environment”. Journal of the ACM, 1999
      • Swap WWW graph with network of judgements
        • Competent users point to relevant resources, and relevant resources are pointed to by competent users
    0.25 0.25 0.25 0.25 0.75 0.25 0.50 0.25 0.75 1.00 1.25 0.75 0.20 0.27 0.33 0.20
  • REALISATION OF THE APPROACH
    • Evaluating Competence
      • HITS suffers from the Tightly Knit Community syndrome
        • SALSA
          • To solve TKC, divide each weight in forward step by node outdegree, and in backward step by authority indegree
          • Side effect: Niche judgements are rewarded more
      • HITS misses on subjectivity
        • Set the only non-zero weight hub being the evaluating node itself
        • At each iteration, stop with probability (1-  )
          • Low  implies faster convergence and higher subjectivity
    R. Lempel and S. Moran. “SALSA: the Stochastic Approach to for Link Structure Analysis”. ACM TOIS, 2001
  • REALISATION OF THE APPROACH
    • Social Filtering
      • Culprit of HITS: backward step (having expressed a judgement is no guarantee of honesty)
    • SOFIA
      • Step 1: Pre-compute PPR on the Web of Trust to compute hubs (nodes) reputation as seen by A
      • Step 2: Start subjective version of HITS/SALSA
      • Step 3: At each backward step, redistribute weights from judgements to users proportionally to users’ reputation as computed by PPR
  • REALISATION OF THE APPROACH
    • Parameters: G=(V,E) , V=U U J , reputation vector r computed with PPR on U
    • Returns: vector t’ with ranking of judgements
    • n=size of U, m=size of J
    • t=0 n , t A =1
    • while (not converged) do
    • {forward step: from users to judgements}
    • t’=0 m
    • for all (u,j) in E do
    • t’ j =t’ j + [w uj / (  k in J w uk )]*t u
    • {backward step: from judgements to users}
    • t=0 n ; t A =1-  ;
    • for all (u,j) in E do
    • t u =t u +  * [(w uj *r u )/(  v in U w vj *r v )]*t’ j
    • end while
    • return t’
  • BROWSING THE TAPESTRY – OUTLINE
    • Philosophy of the Approach
      • Intent
      • Competence
      • Social Filtering
    • Realisation of the Approach
      • Intent: Personalised PageRank
      • Competence: HITS
      • Social Filtering: SOFIA
    • Experiments
      • Accuracy
      • Robustness
  • EXPERIMENTS
    • Datasets
      • Citeseer ( http://citeseer.ist.psu.edu )
        • Social network: co-autorship data (A and B are connected if they wrote papers together)
        • Judgements: citations (if X cites Y, then the authors of X make the implicit judgement “Y is relevant”)
        • Highly clustered subset of the whole graph
          • 10,000 authors
          • 182,675 papers
  • EXPERIMENTS
    • Datasets
      • Last.fm ( http://www.last.fm )
        • Social network: explicit friends list
        • Judgements: top 50 listened artists chart for each user (implicit judgement “I like to listen to songs by X”)
        • BFS crawl of
          • 10,000 users
          • 51,654 artists
  • EXPERIMENTS – ACCURACY
    • How to evaluate accuracy?
    • Goal: rank highly (i.e., recommend) judgements that a user would approve
    • Process:
      • Hide a random judgement
      • Run SOFIA
      • If the algorithm performs well, the hidden judgement will have a high ranking
        • Citeseer: guess a missing citation from a paper
        • Last.fm: find a missing artist in a chart
  • EXPERIMENTS – ACCURACY
    • Citeseer
    Parameters:  =0.5,  =0.3 for SOFIA;  =0.3 for PPR;  =0.05 for N-SOFIA 1709 115 30 8 2 1 PPR 1136 63 12 3 1 1 N-SOFIA 855 31 4 1 1 1 SOFIA 90 75 50 25 10 5
  • EXPERIMENTS – ACCURACY
    • Last.fm
    Parameters:  =0.9,  =0.05 for SOFIA;  =0.5 for PPR;  =0.01 for N-SOFIA 16025 2188 344 66 12 5 PPR 6954 822 157 32 6 2 N-SOFIA 7429 992 174 32 6 2 SOFIA 90 75 50 25 10 5
  • EXPERIMENTS – ROBUSTNESS
    • How to evaluate robustness to Sybil attacks?
    • Goal: defend against malicious judgements
    • Process:
      • Create coalition of 100 Sybils
      • Pick random victim A
      • All Sybils copy A’s judgements, then add a link to X
      • Study ranking of X before and after the attack , on the victim and on other nodes
  • EXPERIMENTS – ROBUSTNESS 2583 5 5165 10 38741 25827 12914 No attack - Any 75 50 25 Role k Algorithm
  • EXPERIMENTS – ROBUSTNESS 2297 1285 334 2583 5 4459 2353 559 5165 10 33322 13371 3101 20493 8757 2012 10730 4759 1092 1 10 100 PPR 38741 25827 12914 No attack - Any 75 50 25 Role k Algorithm
  • EXPERIMENTS – ROBUSTNESS 1 34 2297 1285 334 2583 5 1 85 4459 2353 559 5165 10 1 3132 1 1185 1 348 Victim Other N-SOFIA 33322 13371 3101 20493 8757 2012 10730 4759 1092 1 10 100 PPR 38741 25827 12914 No attack - Any 75 50 25 Role k Algorithm
  • EXPERIMENTS – ROBUSTNESS Lower values of  improve attack resilience, at the expense (small) of accuracy 679 2264 41 1082 1 215 1 34 2297 1285 334 2583 5 1386 4409 132 2126 2 391 1 85 4459 2353 559 5165 10 31765 33064 2815 14718 197 5571 11182 19186 1311 8779 74 2649 3406 9599 469 4612 13 1040 Victim Other Victim Other Victim Other 1 10 100 SOFIA 1 3132 1 1185 1 348 Victim Other N-SOFIA 33322 13371 3101 20493 8757 2012 10730 4759 1092 1 10 100 PPR 38741 25827 12914 No attack - Any 75 50 25 Role k Algorithm
  • UNLOCKING THE TAPESTRY
  • UNLOCKING THE TAPESTRY
    • Present Paradox
      • Centralised sharing (i.e., Web 2.0) of distributedly-produced & location-meaningful content
      • Risks
        • Scalability
        • Interoperability
        • Usability
  • UNLOCKING THE TAPESTRY Evaluate Browse Build
  • UNLOCKING THE TAPESTRY HOW TO BUILD AN UNLOCKED TAPESTRY
  • HOW TO BUILD AN UNLOCKED TAPESTRY
    • Content sharing algorithms and frameworks
      • Impact of mobility
        • With Lucia Del Prete, Liam McNamara & Cecilia Mascolo
      • Impact of uncooperative and malicious users
        • With Afra J. Mashhadi & Sonia Ben Mokhtar
  • UNLOCKING THE TAPESTRY HOW TO BROWSE AN UNLOCKED TAPESTRY
  • HOW TO BROWSE AN UNLOCKED TAPESTRY
    • Distributed SOFIA-like algorithms
      • Partial knowledge
        • With Matteo Dell’Amico
    • “ Making sense” of content
      • Folksonomy & Ontology
        • With Valentina Zanardi & Sonia Ben Mokhtar
  • UNLOCKING THE TAPESTRY HOW TO EVALUATE OUR APPROACHES
  • HOW TO EVALUATE
    • Data Processing
      • How to overlay different datasets about people movement/colocation, interests and social relations in a meaningful way
        • With Afra J. Mashhadi & Sonia Ben Mokhtar
    • Data Gathering
      • Market-based scenario
        • With Daniele Quercia & Liam McNamara
  • THANK YOU!
    • Research Group Website
    • http://mobisys.cs.ucl.ac.uk
    • Research Group Blog
    • http://mobblog.cs.ucl.ac.uk/