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

1. Institut für Web Science & Technologien - WeST
Trust

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. Grimm
Leiden, July 11, 2012 Steffen Staab 2

3. WWW / WWW /
E-Mail E-Mail
TCP/IP TCP/IP
??
Lying
Deceiving
Vandalism
Eaves dropping
Scams
Stealing
© R. Grimm
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4. How to loose 1,000,000,000 US$ in half a day
Failures:
Low quality of data collecting process, hence
Via @Bauckhage currency of information was not considered
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5. What is trust?
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6. Definition of Trust
http://en.wikipedia.org/wiki/Trust_%28social_sciences%29
Definitions of trust[2][3] typically refer to a situation
characterised by the following aspects:
One party (trustor) is willing to rely on the actions of another
party (trustee);
the situation is directed to the future;
the trustor (voluntarily or forcedly) abandons control over the
actions performed by the trustee;
as a consequence, the trustor is uncertain about the
outcome of the other's actions; he can only develop and
evaluate expectations. The uncertainty involves the risk of
failure or harm to the trustor if the trustee will not behave as
desired.
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7. Core Question
What does it mean
if
I say that I trust person P performing action A
or a
System S trusts person or system P performing action A
?
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8. Trust
Perspectives
Information and communication
Education
Social science
Economic/risk management
Reputation
IT security
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9. Information &
Communication Perspective
on Trust
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10. Communication: Should we trust that people understand what we mean?
Failure: Unintended recipient of information
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11. Asymmetry of Communication
I say or do
You say or do
I say or do
etc.
Time line Time line
© R. Grimm
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12. Give and take
give
In order to
requires
trust
in continuity
take
© R. Grimm
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13. Information and Communication - Credibility
 About the truth of information in which we trust, believability [Metzger 07]:
 Expertise
 Trustworthiness
Sender Receiver
Message
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14. Education Perspective
on Trust
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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 trustworthiness
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16. The education perspective
What do I need to teach someone such that
If he trusts person P performing action A he does not suffer?
[Metzger, 2007]
A system S trusts person P performing action A
if P satisfies the assessment criteria for trust
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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.
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18. Social science Perspective
on Trust
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19.  A system S trusts person P performing action A
if P belongs to trusted group
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20. Social Science – 1
Luhmann:
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 complexity
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21. Social Science – 2: Social Theory of Balance
Friend
Foe
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22. Structural Balance for Groups of 3
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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
relationship
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24. Structural Balance for a Network
Definition:
A network is called structurally balanced if all groups of
triangles 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.
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25. Structural Balance for a Network
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26. Weakly Balanced Networks
Definition of Weak Structural Balance Property:
There is no set of three nodes
such that the edges among them consist of
exactly two positive edges and one negative edge
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27. Weak Structural Balance Property
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28. Risk Management/Economic
perspective
on Trust
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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-Bay
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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 sell
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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!
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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
courses
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33. Reputation Perspective on
Trust
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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 2009
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35. Guerilla Marketing
Failure: Information sources had no reputation from
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third parties!
Steffen Staab 35

36. Reputation perspective
Belief in benevolence vs believe in competence
A system S trusts person P performing action A
if sufficient reputation could be aggregated
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37. Reputation scoring as link prediction
me
Predict which unknown link would also be good
to have
Standard algorithm: find friends-of-friends
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38. Leiden, July 11, 2012 Steffen Staab 38

39. Friend of a friend
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40. Reputation Scoring in Social Networks
• Some variation of link prediction (here is just one – big - family of methods)
• Counting and weighting paths
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41. Leiden, July 11, 2012 Steffen Staab 41

42. Distrust computation
Prediction of negative links
Few networks with negative links (Slashdot zoo)
Several methods for handling negative links available
Social 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 life
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43. IT security perspective
on Trust
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44. Hacked Web Sites: Did government post this?
Failure: IT security failed
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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 proven
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46. Trusted third party
Applications
Commercial transactions: Ebay/paypal,...
Public key infrastructures
 https://www.trustcenter.de , www.cert.dfn.de, many others
3rd Party
A B
© R. Grimm
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47. Conclusion
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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/IP
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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!
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50. References
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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)
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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.
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