1. iTrust: Encounter Based Trust recommendation System Udayan Kumar and Dr. Ahmed Helmy

2. Encounter based Trust: iTrust Questions/Motivation – Although so powerful yet these devices do not fully utilize peer to peer interactions. Why? – What can we do if we start leveraging the power of P2P communication in mobile networks? Applications are plenty. 2

3. Need for Trust To break psychological barriers in using AdHoc and Peer-to-Peer mobile services. (I don’t know anything about this device/user. Should I communicate?). Will people communicate without knowledge of other devices? Bootstrap recommendation, reputation or credit based system. (I believe in yellow credit and you believe in green. I think green is fraudulent, why should I trust you). 3

4. Promise of iTrust To be a unified communication oriented trust recommendation framework for mobile devices To capture socially relevant trust information using social science principle of homophiliy[ Mcp01 ] Allow proximity based interactions unavailable in wired networks. Out-of-band communication (can be secured using key exchanges [Che08]) Encourage interactions in mobile societies and adoption of new mobile services. 4

5. Illustration: iTrust I recommend Device B due to high trust score. It has encountered you at …. AB Hey B. Can we Hang out? Hey A. Yes, why not! Out-of-band Key Exchange Lets exchange keys iTrust helped A and B discover each other. They may turn out be lifelong friends. 5

6. Definitions in Literature 1.makes cooperative endeavors happen (e.g., Arrow, 1974; Deutsch, 1973; Gambetta, 1988) 2.cooperating or task coordinating (e.g., Solomon, 1960); 3.placing resources or authority in the other party's hands (Coleman, 1990; Shapiro, 1987a); 4.being influenced by the other (e.g., Bonoma, 1976); 5.committing to a possible loss based on the other's actions (Anderson & Narus, 1990); 6.placing resources or authority in the other party's hands (Coleman, 1990; Shapiro, 1987a); 7.providing open/honest information (e.g., Mishra, 1993); 8.entering informal agreements (Currall & Judge, 1995); 9.increasing one's vulnerability (e.g., Zand, 1972); 10.reducing one's control over the other (Dobing, 1993); 11.risk taking (e.g., Coleman, 1990; Mayer, Davis & Schoorman, 1995); 12.increasing the scope of the other person's discretionary power (Baier, 1986); 13.reducing the rules we place on the other's behavior (Fox, 1974) 14.involving subordinates in decision making (Carnevale & Wechsler, 1992). Trust 6 Actual human trust Context Social Interactions Online social networks Real World interactions Face to Face Interactions Similarity (Homophiliy) --

7. Goals Stability – In trust recommendations Distributed Operation - In calculations Privacy-Preservation – minimize the need of data exchange. Accuracy – when measuring similarity Resilience – from anomalies such as artificially induced encounters. Energy Efficiency 7

8. Architecture Overview 8 Trust Scores

9. Trust Adviser Filters Frequency of Encounter (FE) -- Encounter count Duration of Encounter (DE) – Encounter duration Proposed: Profile Vector – Location based similarity using vectors. Location Vector – Location based similarity using vectors – Count and Duration (Privacy preserving) Behavior Matrix – Location based similarity (using matrix) – Count and Duration [HSU08] 9

10. Filters 10 B’s Profile Vector A’s Profile Vector Profile Vector Exchange for similarity calculations BA B Profile Vector (PV): Location Vector (LV) : Maintains a vector for itself Creates and manages vector for every user encountered Vector for other users are populated with only the information B has witnessed No exchange of vectors is needed !! Privacy preserving Each cell represents a location (such as dorm, ofc) Each cell stores count/duration at that location Vector 4 3232 1515 - - L1 L2 L3 --

11. 11 4 3232 1515 - - - - - - - - - - - - - - - - - - - - - - Day 1 Day 2 Day N Behavior Matrix (BM): B’s Matrix Summary A’s Matrix Summary Behavior Matrix Exchange for similarity calculations BA Maintains a Matrix for itself This matrix is summarized using SVD. The summary is exchanged b/w the users to calculate similariy Each cell stores count/duration at that location

12. Combined Filter (H) In Combined filter we combine trust scores from all the filters to provide a unified trust score. H (U j ) = Σ α i F i (U j ), where α i is the weight for Filter F i, n is the total number of filters Different people may prefer different weights (observed from the user feedback on implementation). Eventually it can be made adaptive. 12 n