What is Trust? e Trust is fundamental in transactions interactions . and communications of human life Psychologically, trust is defined as a kind of subjective behavior Sociologically, trust is a means for reducing the complexity of society 4 Mathematically, trust has been studied as a measurable variable, especially as a probability value o Trust is also related to cooperation, recommendation and reputation 11 May 29, 2009 CSE CUHK
11 May 29, 2009 CSE CUHK What is Trust? ◆ Trust is fundamental in transactions, interactions, and communications of human life. ◆ Psychologically, trust is defined as a kind of subjective behavior. ◆ Sociologically, trust is a means for reducing the complexity of society. ◆ Mathematically, trust has been studied as a measurable variable, especially as a probability value. ◆ Trust is also related to cooperation, recommendation, and reputation
Why Trust for MANET? ◆ Node relationships in ◆ Properties of trust MANET relationships Care about a certain functions a Relativity a Can exist in each node pair a Pervasiveness Good or bad nodes Asymmetry a Information sharing ■ Transitivity a Based on past evidences a Measurability a Lack of enough information Uncertainty 12 May 29, 2009 CSE CUHK
12 May 29, 2009 CSE CUHK Why Trust for MANET? ◆ Properties of trust relationships ◼ Relativity ◼ Pervasiveness ◼ Asymmetry ◼ Transitivity ◼ Measurability ◼ Uncertainty ◆ Node relationships in MANET ◼ Care about a certain functions ◼ Can exist in each node pair ◼ Good or bad nodes ◼ Information sharing ◼ Based on past evidences ◼ Lack of enough information
Our Trust mode e We choose subjective logic trust model as the basis of our trust model because it a best expresses the subjectivity of trust best exhibits the properties of trust relationship in MANET, especially the uncertainty a is more informative than single value trust representation a is more reasonable with probability representation than discrete value representation a is more flexible than upper/lower bound trust representation e We derive our trust model from subjective logic as follows 13 May 29, 2009 CSE CUHK
13 May 29, 2009 CSE CUHK Our Trust Model ◆ We choose subjective logic trust model as the basis of our trust model, because it ◼ best expresses the subjectivity of trust; ◼ best exhibits the properties of trust relationship in MANET, especially the uncertainty; ◼ is more informative than single value trust representation; ◼ is more reasonable with probability representation than discrete value representation; ◼ is more flexible than upper/lower bound trust representation. ◆ We derive our trust model from subjective logic as follows
Trust Representation 4 Denote opinion ar=(bB, dr, ur)to represent the belief from node A to node b Uncertainty PB - Probability that node a believe in node B Probability that node a disbelieve o(40.10.5) 0 In node B Probability of node as uncertainty about b's trustworthiness Disbelief A B +datum=1 B Probability axis 0.5 a The relative atomicity ag is set to 0.5 in our application The probability expectation E(Or)=bg +ag ur 14 May 29, 2009 CSE CUHK
14 May 29, 2009 CSE CUHK Trust Representation ◆ Denote opinion to represent the belief from node A to node B ◼ -- Probability that node A believe in node B ◼ -- Probability that node A disbelieve in node B ◼ -- Probability of node A’s uncertainty about B’s trustworthiness ◼ ◼ The relative atomicity is set to 0.5 in our application. ◼ The probability expectation A B b ( , , ) A B A B A B A B b d u A B d A B u + + =1 A B A B A bB d u A aB 0 0 0 1 1 1 (0.4,0.1,0.5) A B 0.5 Disbelief Uncertainty Belief 0 0.5 1 0.5 0.5 Probability axis A B a ( ) A E B A B A B A B A E(B ) = b + a u
Trust Mapping Between Evidence and Opinion Space Mapping from evidence space to opinion space A +n+ A n B p+n+ 2 ptn Mapping from opinion space to evidence space n=2, where uR≠0 B p: positive evidences ■n: negative evidences 15 May 29, 2009 CSE CUHK
15 May 29, 2009 CSE CUHK Trust Mapping Between Evidence and Opinion Space ◆ Mapping from evidence space to opinion space: ◆ Mapping from opinion space to evidence space: ◼ p : positive evidences ◼ n : negative evidences 2 2 2 2 + + + + + + = = = p n p n n p n p A B A B A B u d b , 0 2 / 2 / = = A B A B A B A B A B where u p b u n d u