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.

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ARM'08 - Keynote Talk

  1. Trust in Pervasive Social Computing Licia Capra Dept of Computer Science University College London
  2. EVOLUTION OF MOBILE TECHNOLOGY
  3. TRANSFORMATION OF INTERNET USERS
  4. A DIGITAL TAPESTRY
  5. PERVASIVE SOCIAL COMPUTING
  6. A DIGITAL TAPESTRY <ul><li>Millions of different resources available to consumers </li></ul><ul><li>Content overload </li></ul>
  7. A DIGITAL TAPESTRY <ul><li>Users need help to find interesting content </li></ul><ul><li>An enormous market for niche content is appearing </li></ul>
  8. CONNECTING SYPPLY & DEMAND <ul><li>Filters </li></ul><ul><li>are essential </li></ul><ul><li>to connect </li></ul><ul><li>supply </li></ul><ul><li>and </li></ul><ul><li>demand </li></ul>
  9. COLLABORATIVE FILTERING <ul><ul><li>Quantify user similarity </li></ul></ul><ul><ul><li>Similar users are deemed competent </li></ul></ul><ul><ul><li>Limitations </li></ul></ul><ul><ul><li>Create recommendations based on behaviour of similar users </li></ul></ul><ul><ul><ul><li>Mass market </li></ul></ul></ul><ul><ul><ul><li>Subject to abuse </li></ul></ul></ul>
  10. SOCIAL NETWORKS <ul><ul><li>Limitations </li></ul></ul><ul><ul><ul><li>No subjectivity </li></ul></ul></ul><ul><ul><ul><li>No transitivity </li></ul></ul></ul><ul><ul><li>Determine user friendship </li></ul></ul><ul><ul><li>Friends are deemed to have good intent </li></ul></ul><ul><ul><li>Share content across friendship relations </li></ul></ul>
  11. 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
  12. BROWSING THE TAPESTRY – OUTLINE <ul><li>Philosophy of the Approach </li></ul><ul><ul><li>Intent </li></ul></ul><ul><ul><li>Competence </li></ul></ul><ul><ul><li>Social Filtering </li></ul></ul><ul><li>Realisation of the Approach </li></ul><ul><ul><li>Intent: Personalised PageRank </li></ul></ul><ul><ul><li>Competence: HITS </li></ul></ul><ul><ul><li>Social Filtering: SOFIA </li></ul></ul><ul><li>Experiments </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><li>Robustness </li></ul></ul>
  13. BROWSING THE TAPESTRY – OUTLINE <ul><li>Philosophy of the Approach </li></ul><ul><ul><li>Intent </li></ul></ul><ul><ul><li>Competence </li></ul></ul><ul><ul><li>Social Filtering </li></ul></ul><ul><li>Realisation of the Approach </li></ul><ul><ul><li>Intent: Personalised PageRank </li></ul></ul><ul><ul><li>Competence: HITS </li></ul></ul><ul><ul><li>Social Filtering: SOFIA </li></ul></ul><ul><li>Experiments </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><li>Robustness </li></ul></ul>
  14. PHILOSOPHY OF THE APPROACH <ul><li>Intent </li></ul>
  15. PHILOSOPHY OF THE APPROACH <ul><li>Intent: willingness to provide honest judgements </li></ul>A B C D Direct Trust Inferred Trust <ul><ul><li>Web of Trust: social network where A is connected to B if A trusts that B behaves honestly </li></ul></ul><ul><ul><li>Transitivity Pattern: propagate trust over intent (e.g., “I trust the friends of my friends”) </li></ul></ul><ul><ul><li>Limitation: transitivity does not cater for the taste of users (i.e., subjectivity in niche markets) </li></ul></ul>
  16. PHILOSOPHY OF THE APPROACH <ul><li>Competence </li></ul>
  17. PHILOSOPHY OF THE APPROACH <ul><li>Competence: ability to provide correct judgements </li></ul>A B X Y Direct Trust Inferred Trust <ul><ul><li>Bipartite graph: network of judgements where user A is connected to judgement X if A expressed X </li></ul></ul><ul><ul><li>Co-citation Pattern: propagate trust over competence (e.g., “I agree with judgements of competent users”) </li></ul></ul><ul><ul><li>Limitation: co-citation subject to abuse (i.e., Sybil attack, profile injection, shilling, etc.) </li></ul></ul>
  18. = Intent & Competence
  19. PHILOSOPHY OF THE APPROACH <ul><li>Social Filtering Pattern </li></ul><ul><ul><li>A can infer trust for judgement Y expressed by D if </li></ul></ul><ul><ul><ul><li>There exists a directed path from A to D in the web of trust </li></ul></ul></ul><ul><ul><ul><ul><li>D is trusted to be well intentioned (robustness) </li></ul></ul></ul></ul><ul><ul><ul><li>A and expressed at least one common judgement </li></ul></ul></ul><ul><ul><ul><ul><li>D is trusted to be competent (accuracy) </li></ul></ul></ul></ul>A D X Y Direct Trust for Judgements Direct Trust for Users C B Inferred Trust for Judgements
  20. BROWSING THE TAPESTRY – OUTLINE <ul><li>Philosophy of the Approach </li></ul><ul><ul><li>Intent </li></ul></ul><ul><ul><li>Competence </li></ul></ul><ul><ul><li>Social Filtering </li></ul></ul><ul><li>Realisation of the Approach </li></ul><ul><ul><li>Intent: Personalised PageRank </li></ul></ul><ul><ul><li>Competence: HITS </li></ul></ul><ul><ul><li>Social Filtering: SOFIA </li></ul></ul><ul><li>Experiments </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><li>Robustness </li></ul></ul>
  21. REALISATION OF THE APPROACH <ul><li>Evaluating Intent </li></ul><ul><ul><li>Desirable properties </li></ul></ul><ul><ul><ul><li>Longer paths disperse trust </li></ul></ul></ul><ul><ul><ul><li>Adding paths increase trust </li></ul></ul></ul><ul><ul><ul><li>Limit amount of trust propagated through attack edges </li></ul></ul></ul>X A B C D <ul><ul><li>PageRank </li></ul></ul><ul><ul><ul><li>Goal: rank importance of web pages </li></ul></ul></ul><ul><ul><ul><li>Intuition: an authoritative page is linked by many authoritative pages </li></ul></ul></ul><ul><ul><li>Swap WWW graph with Web of Trust </li></ul></ul><ul><ul><ul><li>Goal: rank trustworthiness of users </li></ul></ul></ul><ul><ul><ul><li>Intuition: an honest user is being trusted by many honest users </li></ul></ul></ul>Lawrence Page, Sergey Brin, Rajeev Motwani, Terry Winograd. “ The PageRank Citation Ranking: Bringing Order to the Web”. Stanford Digital Library. 1998
  22. REALISATION OF THE APPROACH <ul><li>Evaluating Intent </li></ul><ul><ul><li>Personalised PageRank </li></ul></ul><ul><ul><ul><li>Start random walk at (honest) evaluating node A </li></ul></ul></ul><ul><ul><ul><li>Jump from X to Y with probability proportional to edge weight </li></ul></ul></ul><ul><ul><ul><li>Stop with probability 1-  </li></ul></ul></ul><ul><ul><li>Importance of  </li></ul></ul><ul><ul><ul><li>Max rank for Sybil region: p /(1-  ) </li></ul></ul></ul><ul><ul><ul><li>Low  implies shorter paths, higher robustness, faster convergence but we don’t trust honest users when they are socially far away </li></ul></ul></ul>
  23. REALISATION OF THE APPROACH <ul><li>Evaluating Competence </li></ul><ul><ul><li>HITS </li></ul></ul><ul><ul><ul><li>Web pages are seen as hubs (pages that link to relevant documents, i.e., authorities) and authorities (pages whose content satisfy a query) </li></ul></ul></ul><ul><ul><ul><li>Intuition: good hubs points to good authorities, and good authorities are pointed to by good hubs </li></ul></ul></ul>J. M. Kleinberg. “Authoritative Sources in a Hyperlinked Environment”. Journal of the ACM, 1999 <ul><ul><li>Swap WWW graph with network of judgements </li></ul></ul><ul><ul><ul><li>Competent users point to relevant resources, and relevant resources are pointed to by competent users </li></ul></ul></ul>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
  24. REALISATION OF THE APPROACH <ul><li>Evaluating Competence </li></ul><ul><ul><li>HITS suffers from the Tightly Knit Community syndrome </li></ul></ul><ul><ul><ul><li>SALSA </li></ul></ul></ul><ul><ul><ul><ul><li>To solve TKC, divide each weight in forward step by node outdegree, and in backward step by authority indegree </li></ul></ul></ul></ul><ul><ul><ul><ul><li>Side effect: Niche judgements are rewarded more </li></ul></ul></ul></ul><ul><ul><li>HITS misses on subjectivity </li></ul></ul><ul><ul><ul><li>Set the only non-zero weight hub being the evaluating node itself </li></ul></ul></ul><ul><ul><ul><li>At each iteration, stop with probability (1-  ) </li></ul></ul></ul><ul><ul><ul><ul><li>Low  implies faster convergence and higher subjectivity </li></ul></ul></ul></ul>R. Lempel and S. Moran. “SALSA: the Stochastic Approach to for Link Structure Analysis”. ACM TOIS, 2001
  25. REALISATION OF THE APPROACH <ul><li>Social Filtering </li></ul><ul><ul><li>Culprit of HITS: backward step (having expressed a judgement is no guarantee of honesty) </li></ul></ul><ul><li>SOFIA </li></ul><ul><ul><li>Step 1: Pre-compute PPR on the Web of Trust to compute hubs (nodes) reputation as seen by A </li></ul></ul><ul><ul><li>Step 2: Start subjective version of HITS/SALSA </li></ul></ul><ul><ul><li>Step 3: At each backward step, redistribute weights from judgements to users proportionally to users’ reputation as computed by PPR </li></ul></ul>
  26. REALISATION OF THE APPROACH <ul><li>Parameters: G=(V,E) , V=U U J , reputation vector r computed with PPR on U </li></ul><ul><li>Returns: vector t’ with ranking of judgements </li></ul><ul><li>n=size of U, m=size of J </li></ul><ul><li>t=0 n , t A =1 </li></ul><ul><li>while (not converged) do </li></ul><ul><li>{forward step: from users to judgements} </li></ul><ul><li>t’=0 m </li></ul><ul><li>for all (u,j) in E do </li></ul><ul><li>t’ j =t’ j + [w uj / (  k in J w uk )]*t u </li></ul><ul><li>{backward step: from judgements to users} </li></ul><ul><li>t=0 n ; t A =1-  ; </li></ul><ul><li>for all (u,j) in E do </li></ul><ul><li>t u =t u +  * [(w uj *r u )/(  v in U w vj *r v )]*t’ j </li></ul><ul><li>end while </li></ul><ul><li>return t’ </li></ul>
  27. BROWSING THE TAPESTRY – OUTLINE <ul><li>Philosophy of the Approach </li></ul><ul><ul><li>Intent </li></ul></ul><ul><ul><li>Competence </li></ul></ul><ul><ul><li>Social Filtering </li></ul></ul><ul><li>Realisation of the Approach </li></ul><ul><ul><li>Intent: Personalised PageRank </li></ul></ul><ul><ul><li>Competence: HITS </li></ul></ul><ul><ul><li>Social Filtering: SOFIA </li></ul></ul><ul><li>Experiments </li></ul><ul><ul><li>Accuracy </li></ul></ul><ul><ul><li>Robustness </li></ul></ul>
  28. EXPERIMENTS <ul><li>Datasets </li></ul><ul><ul><li>Citeseer ( http://citeseer.ist.psu.edu ) </li></ul></ul><ul><ul><ul><li>Social network: co-autorship data (A and B are connected if they wrote papers together) </li></ul></ul></ul><ul><ul><ul><li>Judgements: citations (if X cites Y, then the authors of X make the implicit judgement “Y is relevant”) </li></ul></ul></ul><ul><ul><ul><li>Highly clustered subset of the whole graph </li></ul></ul></ul><ul><ul><ul><ul><li>10,000 authors </li></ul></ul></ul></ul><ul><ul><ul><ul><li>182,675 papers </li></ul></ul></ul></ul>
  29. EXPERIMENTS <ul><li>Datasets </li></ul><ul><ul><li>Last.fm ( http://www.last.fm ) </li></ul></ul><ul><ul><ul><li>Social network: explicit friends list </li></ul></ul></ul><ul><ul><ul><li>Judgements: top 50 listened artists chart for each user (implicit judgement “I like to listen to songs by X”) </li></ul></ul></ul><ul><ul><ul><li>BFS crawl of </li></ul></ul></ul><ul><ul><ul><ul><li>10,000 users </li></ul></ul></ul></ul><ul><ul><ul><ul><li>51,654 artists </li></ul></ul></ul></ul>
  30. EXPERIMENTS – ACCURACY <ul><li>How to evaluate accuracy? </li></ul><ul><li>Goal: rank highly (i.e., recommend) judgements that a user would approve </li></ul><ul><li>Process: </li></ul><ul><ul><li>Hide a random judgement </li></ul></ul><ul><ul><li>Run SOFIA </li></ul></ul><ul><ul><li>If the algorithm performs well, the hidden judgement will have a high ranking </li></ul></ul><ul><ul><ul><li>Citeseer: guess a missing citation from a paper </li></ul></ul></ul><ul><ul><ul><li>Last.fm: find a missing artist in a chart </li></ul></ul></ul>
  31. EXPERIMENTS – ACCURACY <ul><li>Citeseer </li></ul>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
  32. EXPERIMENTS – ACCURACY <ul><li>Last.fm </li></ul>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
  33. EXPERIMENTS – ROBUSTNESS <ul><li>How to evaluate robustness to Sybil attacks? </li></ul><ul><li>Goal: defend against malicious judgements </li></ul><ul><li>Process: </li></ul><ul><ul><li>Create coalition of 100 Sybils </li></ul></ul><ul><ul><li>Pick random victim A </li></ul></ul><ul><ul><li>All Sybils copy A’s judgements, then add a link to X </li></ul></ul><ul><ul><li>Study ranking of X before and after the attack , on the victim and on other nodes </li></ul></ul>
  34. EXPERIMENTS – ROBUSTNESS 2583 5 5165 10 38741 25827 12914 No attack - Any 75 50 25 Role k Algorithm
  35. 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
  36. 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
  37. 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
  38. UNLOCKING THE TAPESTRY
  39. UNLOCKING THE TAPESTRY <ul><li>Present Paradox </li></ul><ul><ul><li>Centralised sharing (i.e., Web 2.0) of distributedly-produced & location-meaningful content </li></ul></ul><ul><ul><li>Risks </li></ul></ul><ul><ul><ul><li>Scalability </li></ul></ul></ul><ul><ul><ul><li>Interoperability </li></ul></ul></ul><ul><ul><ul><li>Usability </li></ul></ul></ul>
  40. UNLOCKING THE TAPESTRY Evaluate Browse Build
  41. UNLOCKING THE TAPESTRY HOW TO BUILD AN UNLOCKED TAPESTRY
  42. HOW TO BUILD AN UNLOCKED TAPESTRY <ul><li>Content sharing algorithms and frameworks </li></ul><ul><ul><li>Impact of mobility </li></ul></ul><ul><ul><ul><li>With Lucia Del Prete, Liam McNamara & Cecilia Mascolo </li></ul></ul></ul><ul><ul><li>Impact of uncooperative and malicious users </li></ul></ul><ul><ul><ul><li>With Afra J. Mashhadi & Sonia Ben Mokhtar </li></ul></ul></ul>
  43. UNLOCKING THE TAPESTRY HOW TO BROWSE AN UNLOCKED TAPESTRY
  44. HOW TO BROWSE AN UNLOCKED TAPESTRY <ul><li>Distributed SOFIA-like algorithms </li></ul><ul><ul><li>Partial knowledge </li></ul></ul><ul><ul><ul><li>With Matteo Dell’Amico </li></ul></ul></ul><ul><li>“ Making sense” of content </li></ul><ul><ul><li>Folksonomy & Ontology </li></ul></ul><ul><ul><ul><li>With Valentina Zanardi & Sonia Ben Mokhtar </li></ul></ul></ul>
  45. UNLOCKING THE TAPESTRY HOW TO EVALUATE OUR APPROACHES
  46. HOW TO EVALUATE <ul><li>Data Processing </li></ul><ul><ul><li>How to overlay different datasets about people movement/colocation, interests and social relations in a meaningful way </li></ul></ul><ul><ul><ul><li>With Afra J. Mashhadi & Sonia Ben Mokhtar </li></ul></ul></ul><ul><li>Data Gathering </li></ul><ul><ul><li>Market-based scenario </li></ul></ul><ul><ul><ul><li>With Daniele Quercia & Liam McNamara </li></ul></ul></ul>
  47. THANK YOU! <ul><li>Research Group Website </li></ul><ul><li>http://mobisys.cs.ucl.ac.uk </li></ul><ul><li>Research Group Blog </li></ul><ul><li>http://mobblog.cs.ucl.ac.uk/ </li></ul>

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