Institut für Web Science & Technologien - WeST. Trust

Transcription

1 Institut für Web Science & Technologien - WeST Trust

2 Web Noone owns the Web Everyone can participate, share,... No central control No centrally ensured security WWW / TCP/IP WWW / TCP/IP R. Grimm Steffen Staab 2

3 WWW / TCP/IP WWW / TCP/IP?? Lying Deceiving Vandalism Eaves dropping Scams Stealing R. Grimm Steffen Staab 3

4 How to loose 1,000,000,000 US$ in half a day Failures: Low quality of data collecting process, hence currency of information was not considered Steffen Staab 4

5 WHAT IS TRUST? Steffen Staab 5

6 Definition of Trust 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. Steffen Staab 6

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? Steffen Staab 7

8 Trust Perspectives Information and communication Education Social science Economic/risk management Reputation IT security Steffen Staab 8

9 INFORMATION & COMMUNICATION PERSPECTIVE ON TRUST Steffen Staab 9

10 Communication: Should we trust that people understand what we mean? Failure: Unintended recipient of information Steffen Staab 10

11 Asymmetry of Communication I say or do You say or do I say or do etc. R. Grimm Time line Time line R. Grimm Steffen Staab 11

12 Give and take give In order to requires trust in continuity take R. Grimm R. Grimm Steffen Staab 12

13 Information and Communication - Credibility About the truth of information in which we trust, believability [Metzger 07]: w Expertise w Trustworthiness Receiver Sender Message Steffen Staab 13

14 EDUCATION PERSPECTIVE ON TRUST Steffen Staab 14

15 Online Investment Scams Trust Risk (greed) Identity of debitors BTW: I am not sure you can trust this website Failure: Identity scam & users missassess trustworthiness Steffen Staab 15

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 Steffen Staab 16

17 Credibility: What people do and what they should do (Teach to) Assess credibility by checklist: w accuracy, w authority, Identity, qualifications Whois, Traceroute, NSlookup/Dig w objectivity, w currency, w and coverage or scope Can be reasoned with in the semantic web if described! (eg. [Schenk]) Internet users may be easily Assessing what people do on the Web: Interestingly, what focus-group participants said they looked for in duped assessing by credibility slick Web was design. not what the researchers found they actually looked at during the observational portion of the study. meta strategies Steffen Staab 17

18 SOCIAL SCIENCE PERSPECTIVE ON TRUST Steffen Staab 18

19 A system S trusts person P performing action A if P belongs to trusted group Steffen Staab 19

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 Steffen Staab 20

21 Social Science 2: Social Theory of Balance Friend Foe Steffen Staab 21

22 Structural Balance for Groups of 3 Steffen Staab 22

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 Steffen Staab 23

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, w such that each pair of people in X likes each other, w each pair of people in Y likes each other, w and everyone in X is the enemy of everyone in Y. Steffen Staab 24

25 Structural Balance for a Network Steffen Staab 25

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 Steffen Staab 26

27 Weak Structural Balance Property Steffen Staab 27

28 RISK MANAGEMENT/ ECONOMIC PERSPECTIVE ON TRUST Steffen Staab 28

29 Risk Management/Economic perspective Risk is a pair w Value/Cost of an event arising w 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 Steffen Staab 29

30 A Web Market Example Trust issues arising in markets with information asymmetry e.g. E-Bay w Assume 50 good cars, 50 bad cars could be for sale w 200 buyers willing to buy w Assume buyers are willing to pay up to 12 for good cars and up to 6 for bad cars w Assume sellers are willing to sell from 10 upwards and 5 upwards for good and bad cars respectively Information Asymmetry: w Sellers judge good/bad accurately w Buyers cannot judge good/bad at all, but know about willingness of sellers to sell Steffen Staab 30

31 Economic perspective: Expected Value Information Asymmetry: w Expected value of a car for a buyer at most (12+6)/2=9 w 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 w 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! Steffen Staab 31

32 Asymmetric information and Trust signals Used car markets w Partial remedy Guarantees by traders Labor market Reduces subsequent costs for buyers of lemons Strong signal that the car has decent quality w Partial remedy: Insurance Education certificates Education leads to knowledge Certificate is signal for intellectual and work capacity w Buyer of insurance knows more Very partial remedy: incentives system to take sports courses Steffen Staab 32

33 REPUTATION PERSPECTIVE ON TRUST Steffen Staab 33

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 Steffen Staab 34

35 Guerilla Marketing Failure: Information sources had no reputation from 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 Steffen Staab 36

37 Reputation scoring as link prediction me Predict which unknown link would also be good to have Standard algorithm: find friends-of-friends Steffen Staab 37

38 Steffen Staab 38

39 Friend of a friend Steffen Staab 39

40 Reputation Scoring in Social Networks Some variation of link prediction (here is just one big - family of methods) Counting and weighting paths Steffen Staab 40

41 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] w Age gap w Low number of common friends (embeddedness) w No common female friend w One neurotic or introvert Results seem to be comparable to unlinking in real life Steffen Staab 42

43 IT SECURITY PERSPECTIVE ON TRUST Steffen Staab 43

44 Hacked Web Sites: Did government post this? Failure: IT security failed Steffen Staab 44

45 Security perspective Authorization w Specific person P is allowed to do action A Authentication w 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 Steffen Staab 45

46 Trusted third party Applications Commercial transactions: Ebay/paypal,... Public key infrastructures w many others 3rd Party A B R. Grimm Steffen Staab 46

47 CONCLUSION Steffen Staab 47

48 Trust AND Web Data How does Trust deviate for Web Data? w People are coupled more loosely Fewer possibilities for Reputation building Personal ties w Increased chance of encountering misbehavior WWW / TCP/IP Decentralization on the Web w Web data does not focus trust it only extends the issue WWW / TCP/IP Steffen Staab 48

49 Conclusion Survey of trust issues w Incomplete w Interdisciplinary w Interwoven With each other E.g. trust/reputation as computed from social network analysis With further Web topics We need w Experiments w Models w Analytic techniques So far: strengths in one of these areas, but not in all! Steffen Staab 49

50 REFERENCES Steffen Staab 50

51 Survey type articles Luhmann: Vertrauen - ein Mechanismus der Reduktion sozialer Komplexität (1968) N. Luhmann, Trust and Power. John Wiley & Sons, 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 , Donovan Artz, Yolanda Gil: A survey of trust in computer science and the Semantic Web. J. Web Sem. 5(2): (2007) Jennifer Golbeck (2008) "Trust on the World Wide Web: A Survey", Foundations and Trends in Web Science: Vol. 1: No 2, pp / Piotr Cofta (2011) "The Trustworthy and Trusted Web", Foundations and Trends in Web Science: Vol. 2: No 4, pp Miriam J. Metzger: Making sense of credibility on the Web: Models for evaluating online information and recommendations for future research. JASIST 58(13): (2007) Steffen Staab 51

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: Simon Schenk, Renata Queiroz Dividino, Steffen Staab: Using provenance to debug changing ontologies. J. Web Sem. 9(3): (2011) Xian Li, Timothy Lebo, Deborah L. McGuinness: Provenance-Based Strategies to Develop Trust in Semantic Web Applications. IPAW 2010: Luca de Alfaro, Ashutosh Kulshreshtha, Ian Pye, B. Thomas Adler: Reputation systems for open collaboration. Commun. ACM 54(8): (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 Daniele Quercia, Mansoureh Bodaghi, Jon Crowcroft. Loosing Friends on Facebook. In: Proc. WebSci 2012, Evanston, June ACM. Steffen Staab 52