1. 01/02/2016Web security and Trust Management 1 Reza Mousoli 2002-4 Web Security and Trust Management

2. 01/02/2016Web security and Trust Management 2 Context Rapid technological progress Borderless Internet No regulations No LAN, but WAN Perception of victimless crime

3. 01/02/2016Web security and Trust Management 3 Trust a fuzzy word in computer security research Vagueness of common language. objective is, Security in distributed systems. Should Trust in this context be uniform and predictable for the same set of data?? i.e. FIXED.

4. 01/02/2016Web security and Trust Management 4 Trusting Behaviour Trusting Intention Situational Decision to Trust Dispositional Trust Trusting Beliefs System Trust Belief Formation Processes Human Trust Model

5. 01/02/2016Web security and Trust Management 5 The logic is simple. One has Trusting Beliefs about another, one will be willing to depend on that person (Trusting Intention). If one intends to depend on the person, then one will behave in ways that manifest that intention to depend (Trusting Behaviour)

6. 01/02/2016Web security and Trust Management 6 Closed systems security. Distributed system security PKI ad-hoc methods heavily based on the strength of cryptography is the main method of enforcing security on the web. BUT……… Security is only as strong as the weakest link in the chain.

7. 01/02/2016Web security and Trust Management 7 Corporations view of Trust IBM, Microsoft, Verisign, RSA, market and sell products that address security and Trust management problems. Product such as IBM ‘s Trust Authority, VeriSign’s Site Trust Services, MS Domain Trust or RSA Clear Trust; define Trust in their own terms

8. 01/02/2016Web security and Trust Management 8 Corporations view of trust Generally means authorization,PKI, IPsec, public and private key encryption, SSL, SSO. i.e. Static trust. We need Dynamic Trust Management

9. 01/02/2016Web security and Trust Management 9 Buzz Word!

10. 01/02/2016Web security and Trust Management 10 VersiSign Trust Authority IBM RSA ClearTrust ® Kyberpass Trust services Entrust Authority ™ HP Trust Services

11. 01/02/2016Web security and Trust Management 11 In NT, Trusts were limited to the two domains involved in the Trust and the Trust relationship was one-way. In Windows 2000 and above, all Trusts are transitive and two-way. Both domains in a Trust relationship automatically Trust each other. Windows Trust

12. 01/02/2016Web security and Trust Management 12

13. 01/02/2016Web security and Trust Management 13 This means that if Domain A trusts Domain B and Domain B trusts Domain C, users from Domain C (when granted the proper permissions) can access resources in Domain A.

14. 01/02/2016Web security and Trust Management 14 The Trust-management by Blaze “Does the set C of credentials prove that the request r complies with the local security policy P?” - Trust properties for modelling??? - Transitive? - Symmetric?

15. 01/02/2016Web security and Trust Management 15 Trust and Reputation

16. 01/02/2016Web security and Trust Management 16 Trust and Reputation

17. 01/02/2016Web security and Trust Management 17 Reputation Agents

18. 01/02/2016Web security and Trust Management 18 Reputation Model In this model, only 2 ratings by an agent are considered: an “approve” (represented by ‘1’) or “disapprove” (represented by ‘0’) for an object o k in the environment. Let this rating process be represented by: Rating: r :A  O  {1,0} (3.3) where ik r represents the rating by agent a i on object o k. To model the process of opinion sharing between agents, the concept of an encounter is required. An encounter is an event between 2 different agents (a i, a j ) such that the query agent a i asks the response agent a j for a j ’s rating of an object: Encounter: e  E  A 2  O  {0,1}  {  } (3.4)

19. 01/02/2016Web security and Trust Management 19 Trust Rating estimates Trust calculation algorithms:- Fuzzy Logic Logic of Uncertainty Bayesian Logic

20. 01/02/2016Web security and Trust Management 20 Bayesian Estimate Rating Let xab(i) be the indicator variable for a’s approval of b after the ith encounter between them. If a and b have had n encounters in the past, the proportion of number of approvals of b by a can be modeled with a Beta prior distribution: Let n = total number of encounters between a and b in the past p = number of approvals of b by a in the past q = true proportion of number of approvals for b by a qˆ = estimator for q based on all encounters between a and b

21. 01/02/2016Web security and Trust Management 21 Estimate of Approval by the Agent

22. 01/02/2016Web security and Trust Management 22 Future Research Trust calculation algorithms Computable elements of trust Simulation and implementation

23. 01/02/2016Web security and Trust Management 23 Discussions