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Trust

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Invited Talk at Web Science Summer School, Leiden, July 11, 2012

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Trust

  1. 1. Institut für Web Science & Technologien - WeSTTrust
  2. 2. Web WWW / Noone owns the Web E-Mail TCP/IP Everyone can participate, share,.... No central control No centrally ensured security WWW / E-Mail TCP/IP © R. GrimmLeiden, July 11, 2012 Steffen Staab 2
  3. 3. WWW / WWW / E-Mail E-Mail TCP/IP TCP/IP ?? Lying Deceiving Vandalism Eaves dropping Scams Stealing © R. GrimmLeiden, July 11, 2012 Steffen Staab 3
  4. 4. How to loose 1,000,000,000 US$ in half a day Failures: Low quality of data collecting process, henceVia @Bauckhage currency of information was not considered Leiden, July 11, 2012 Steffen Staab 4
  5. 5. What is trust?Leiden, July 11, 2012 Steffen Staab 5
  6. 6. Definition of Trusthttp://en.wikipedia.org/wiki/Trust_%28social_sciences%29Definitions of trust[2][3] typically refer to a situationcharacterised by the following aspects:One party (trustor) is willing to rely on the actions of anotherparty (trustee);the situation is directed to the future;the trustor (voluntarily or forcedly) abandons control over theactions performed by the trustee;as a consequence, the trustor is uncertain about theoutcome of the others actions; he can only develop andevaluate expectations. The uncertainty involves the risk offailure or harm to the trustor if the trustee will not behave asdesired.Leiden, July 11, 2012 Steffen Staab 6
  7. 7. Core QuestionWhat does it meanif I say that I trust person P performing action Aor a System S trusts person or system P performing action A? Leiden, July 11, 2012 Steffen Staab 7
  8. 8. TrustPerspectivesInformation and communicationEducationSocial scienceEconomic/risk managementReputationIT securityLeiden, July 11, 2012 Steffen Staab 8
  9. 9. Information & Communication Perspective on TrustLeiden, July 11, 2012 Steffen Staab 9
  10. 10. Communication: Should we trust that people understand what we mean? Failure: Unintended recipient of information Leiden, July 11, 2012 Steffen Staab 10
  11. 11. Asymmetry of Communication I say or do You say or do I say or do etc. Time line Time line © R. GrimmLeiden, July 11, 2012 Steffen Staab 11
  12. 12. Give and take give In order to requires trust in continuity take © R. GrimmLeiden, July 11, 2012 Steffen Staab 12
  13. 13. Information and Communication - Credibility About the truth of information in which we trust, believability [Metzger 07]:  Expertise  Trustworthiness Sender Receiver MessageLeiden, July 11, 2012 Steffen Staab 13
  14. 14. Education Perspective on TrustLeiden, July 11, 2012 Steffen Staab 14
  15. 15. Online Investment Scams Trust Identity of Risk debitors (greed) BTW: I am not sure you can trust this website Failure: Identity scam & users missassess trustworthinessLeiden, July 11, 2012 Steffen Staab 15
  16. 16. The education perspectiveWhat do I need to teach someone such thatIf he trusts person P performing action A he does not suffer?[Metzger, 2007]A system S trusts person P performing action Aif P satisfies the assessment criteria for trustLeiden, July 11, 2012 Steffen Staab 16
  17. 17. Credibility: What people do and what they should do  (Teach to) Assess credibility by checklist:  accuracy, Can be  authority, reasoned • Identity, qualifications with in the – Whois, Traceroute, NSlookup/Dig semantic  objectivity, web if  currency, described! (eg. [Schenk])  and coverage or scope Internet users may be easily Assessing what people do on the Web: Interestingly, what focus-group participants said they looked for in assessing credibility wasdesign. the researchers found they duped by slick Web not what ⇒meta strategies actually looked at during the observational portion of the study. Leiden, July 11, 2012 Steffen Staab 17
  18. 18. Social science Perspective on TrustLeiden, July 11, 2012 Steffen Staab 18
  19. 19.  A system S trusts person P performing action A if P belongs to trusted groupLeiden, July 11, 2012 Steffen Staab 19
  20. 20. Social Science – 1Luhmann:Levels of increasing freedom to act  Familarity  Based on what we know  No deviation from the known  Confidence  Founded on laws, fallback positions,...  Trust  Acting under risk Trust is the reduction of complexityLeiden, July 11, 2012 Steffen Staab 20
  21. 21. Social Science – 2: Social Theory of Balance Friend FoeLeiden, July 11, 2012 Steffen Staab 21
  22. 22. Structural Balance for Groups of 3Leiden, July 11, 2012 Steffen Staab 22
  23. 23. Structural Balance for Groups of 3 Definition: A triangle is balanced if all 3 relations between the nodes are positive or if there is exactly one positive relationshipLeiden, July 11, 2012 Steffen Staab 23
  24. 24. Structural Balance for a NetworkDefinition:A network is called structurally balanced if all groups oftriangles are structurally balanced.Balance Theorem:If a labeled complete graph is balanced,then either all pairs of nodes are friend,or else the nodes can be divided into two groups, X and Y,  such that each pair of people in X likes each other,  each pair of people in Y likes each other,  and everyone in X is the enemy of everyone in Y.Leiden, July 11, 2012 Steffen Staab 24
  25. 25. Structural Balance for a NetworkLeiden, July 11, 2012 Steffen Staab 25
  26. 26. Weakly Balanced NetworksDefinition of Weak Structural Balance Property:There is no set of three nodessuch that the edges among them consist ofexactly two positive edges and one negative edgeLeiden, July 11, 2012 Steffen Staab 26
  27. 27. Weak Structural Balance PropertyLeiden, July 11, 2012 Steffen Staab 27
  28. 28. Risk Management/Economic perspective on TrustLeiden, July 11, 2012 Steffen Staab 28
  29. 29. Risk Management/Economic perspective Risk is a pair  Value/Cost of an event arising  Probability that the event will arise Trust means willingness to bear a risk A system S trusts person P performing action A if the expected overall value/utility is positive In particular trust issues arise in markets with information asymmetry – e.g. E-BayLeiden, July 11, 2012 Steffen Staab 29
  30. 30. A Web Market Example Trust issues arising in markets with information asymmetry – e.g. E-Bay  Assume 50 good cars, 50 bad cars could be for sale  200 buyers willing to buy  Assume buyers are willing to pay up to 12 for good cars and up to 6 for bad cars  Assume sellers are willing to sell from 10 upwards and 5 upwards for good and bad cars respectively Information Asymmetry:  Sellers judge good/bad accurately  Buyers cannot judge good/bad at all, but know about willingness of sellers to sellLeiden, July 11, 2012 Steffen Staab 30
  31. 31. Economic perspective: Expected Value Information Asymmetry:  Expected value of a car for a buyer at most (12+6)/2=9  At 9 sellers of good cars do not sell, therefore rational buyers cannot expect any good cars to be on the market! • No good cars are sold, because of a lack of trust! Self-fulfilling expectations! Market Failure! Solution: Reputation reduces information asymmetry  If ¾ of cars sold as good cars are good, then expected value is ¾*12+1/4*6=10.5 – i.e. good cars can be sold!Leiden, July 11, 2012 Steffen Staab 31
  32. 32. Asymmetric information and Trust signals Used car markets  Partial remedy • Guarantees by traders – Reduces subsequent costs for buyers of lemons – Strong signal that the car has decent quality Labor market  Partial remedy: • Education certificates – Education leads to knowledge – Certificate is signal for intellectual and work capacity Insurance  Buyer of insurance knows more • Very partial remedy: incentives system to take sports coursesLeiden, July 11, 2012 Steffen Staab 32
  33. 33. Reputation Perspective on TrustLeiden, July 11, 2012 Steffen Staab 33
  34. 34. +++ „Los Angeles (dpa) – In der kalifornischen Kleinstadt Bluewater soll es nach einem Bericht des örtlichen Senders vpk-tv zu einem Selbstmordanschlag gekommen sein. Es habe in einem Restaurant zwei Explosionen gegeben...“ +++ German Press Agency DPA, 10 Sep 2009Leiden, July 11, 2012 Steffen Staab 34
  35. 35. Guerilla Marketing Failure: Information sources had no reputation fromLeiden, July 11, 2012 third parties! Steffen Staab 35
  36. 36. Reputation perspectiveBelief in benevolence vs believe in competenceA system S trusts person P performing action Aif sufficient reputation could be aggregatedLeiden, July 11, 2012 Steffen Staab 36
  37. 37. Reputation scoring as link prediction me Predict which unknown link would also be good to haveStandard algorithm: find friends-of-friendsLeiden, July 11, 2012 Steffen Staab 37
  38. 38. Leiden, July 11, 2012 Steffen Staab 38
  39. 39. Friend of a friendLeiden, July 11, 2012 Steffen Staab 39
  40. 40. Reputation Scoring in Social Networks• Some variation of link prediction (here is just one – big - family of methods)• Counting and weighting paths Leiden, July 11, 2012 Steffen Staab 40
  41. 41. Leiden, July 11, 2012 Steffen Staab 41
  42. 42. Distrust computationPrediction of negative linksFew networks with negative links (Slashdot zoo)Several methods for handling negative links availableSocial factorsFacebook unlinking prediction [Quercia et al]  Age gap  Low number of common friends (embeddedness)  No common female friend  One neurotic or introvertResults seem to be comparable to „unlinking“ in real lifeLeiden, July 11, 2012 Steffen Staab 42
  43. 43. IT security perspective on TrustLeiden, July 11, 2012 Steffen Staab 43
  44. 44. Hacked Web Sites: Did government post this? Failure: IT security failedLeiden, July 11, 2012 Steffen Staab 44
  45. 45. Security perspective Authorization  Specific person P is allowed to do action A Authentication  Proof to be a specific person Sometimes: Tokens that lend authority and/or authentication via centralized or decentralized trust center A system S trusts Person P to perform A if authentication and authorization can be provenLeiden, July 11, 2012 Steffen Staab 45
  46. 46. Trusted third partyApplicationsCommercial transactions: Ebay/paypal,...Public key infrastructures  https://www.trustcenter.de , www.cert.dfn.de, many others 3rd Party A B © R. GrimmLeiden, July 11, 2012 Steffen Staab 46
  47. 47. ConclusionLeiden, July 11, 2012 Steffen Staab 47
  48. 48. Trust AND Web Data WWW / How does Trust deviate for Web Data? E-Mail TCP/IP  People are coupled more loosely • Fewer possibilities for – Reputation building – Personal ties  Increased chance of encountering misbehavior • Decentralization on the Web  Web data does not focus trust – it only extends the issue WWW / E-Mail TCP/IPLeiden, July 11, 2012 Steffen Staab 48
  49. 49. Conclusion Survey of trust issues  Incomplete  Interdisciplinary  Interwoven • With each other – E.g. trust/reputation as computed from social network analysis • With further Web topics We need  Experiments So far:  Models strengths in one of these  Analytic techniques areas, but not in all! Leiden, July 11, 2012 Steffen Staab 49
  50. 50. ReferencesLeiden, July 11, 2012 Steffen Staab 50
  51. 51. Survey type articles Luhmann: Vertrauen - ein Mechanismus der Reduktion sozialer Komplexität (1968) N. Luhmann, Trust and Power. John Wiley & Sons, 1979. Jin-Hee Cho, Ananthram Swami, Ing-Ray Chen, A Survey on Trust Management for Mobile Ad Hoc Networks, IEEE COMMUNICATIONS SURVEYS & TUTORIALS, VOL. 13, NO. 4, FOURTH QUARTER 2011 S. Staab et al., “The Pudding of Trust,” IEEE Intelligent Systems, vol. 19, no. 5, pp. 74-88, 2004. Donovan Artz, Yolanda Gil: A survey of trust in computer science and the Semantic Web. J. Web Sem. 5(2): 58-71 (2007) Jennifer Golbeck (2008) "Trust on the World Wide Web: A Survey", Foundations and Trends in Web Science: Vol. 1: No 2, pp 131-197. http:/dx.doi.org/10.1561/1800000006 Piotr Cofta (2011) "The Trustworthy and Trusted Web", Foundations and Trends in Web Science: Vol. 2: No 4, pp 243-381. http://dx.doi.org/10.1561/1800000016 Miriam J. Metzger: Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research. JASIST 58(13): 2078-2091 (2007)Leiden, July 11, 2012 Steffen Staab 51
  52. 52. Specific articles/books: Jennifer Golbeck PhD Thesis U Maryland Jerome Kunegis PhD Thesis U Koblenz Sepandar D. Kamvar, Mario T. Schlosser, Hector Garcia-Molina: The Eigentrust algorithm for reputation management in P2P networks. WWW 2003: 640-651 Simon Schenk, Renata Queiroz Dividino, Steffen Staab: Using provenance to debug changing ontologies. J. Web Sem. 9(3): 284-298 (2011) Xian Li, Timothy Lebo, Deborah L. McGuinness: Provenance-Based Strategies to Develop Trust in Semantic Web Applications. IPAW 2010: 182-197 Luca de Alfaro, Ashutosh Kulshreshtha, Ian Pye, B. Thomas Adler: Reputation systems for open collaboration. Commun. ACM 54(8): 81-87 (2011) R. Guha, R. Kumar, P. Raghavan, and A. Tomkins, “Propagation of trust and distrust,” in Proceedings of the 13th international conference on World Wide Web. ACM, 2004, pp. 403–412. Daniele Quercia, Mansoureh Bodaghi, Jon Crowcroft. Loosing “Friends” on Facebook. In: Proc. WebSci 2012, Evanston, June 2012. ACM.Leiden, July 11, 2012 Steffen Staab 52

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