1. Trustworthiness Management in the Social Internet of Things
Michele Nitti, Roberto Girau, and Luigi Atzori
Presented by: Chris Morrell, 24 April 2014

2. Agenda Introduction & Background The Proposed Solution
The Subjective Trust Model
The Objective Trust Model
Experimental Results
Conclusion

3. What is the SIoT
A social network where every node is an object capable of establishing social relationships with other things in an autonomous way according to rules set by the owner.

4. Purpose
Address the uncertainty of trust regarding other nodes and to suggest strategies to establish trustworthiness among nodes.

5. SIoT Relationships Relationships Parental Object Relationship (POR)
Built in same period, by same manufacturer
Co-Location Object Relationship (CLOR)
Live in a common location (cohabitation)
Co-Work Object Relationship (CWOR)
Works in a common location (work)
Ownership Object Relationship (OOR)
Belong to the same owner
Social Object Relationship (SOR)
Objects that come into contact due to owner relationships

6. SIoT Architecture SIoT Architecture Relationship Management
The intelligence that allows objects to start, update, and terminate relationships
Service Discovery
Find the object that provides the desired service
Service Composition
Enables interaction among objects
Trustworthiness Management
How to know which services and objects to trust

7. Properties of Trustworthiness
Transitivity: Depends on the purpose
Composability: Combining recommendations
Personalization: Differing opinions matter
Asymmetric: See personalization

8. Notation The set of nodes: An undirected graph describing the network:
A node i’s neighborhood
Common friends between pi and pj

9. Notation (cont.) The set of services provided by pj
The set of nodes that provides service h
The set of edges that represents the path from pi to pj

10. An Example Graph
Nodes p1 – p10 where node p1 is requesting service S10
Z10={p5}
R1,5={p1p4,p4p8,p8p5}
N1={p2,p3,p4} (Friends)
K1,4={p2,p4} (Mutual Friends)

11. Objects mimic human social behavior
The Proposed Solution
Objects mimic human social behavior

12. Trust Models Subjective Trustworthiness Objective Trustworthiness
Trust is local and based on experience of the local node and its friends
Trust is transitive, composable, personal, and asymmetric
Objective Trustworthiness
Trust is network centric and is managed by Pre-Trusted Objects (PTOs) and based on experiences of all nodes
Trust is only composable (not transitive, personal, or assymmetric)

13. Estimating Reputation
Feedback (flij)
Rate an experience from 0 to 1.
Total Number of Transactions (Nij)
Are the nodes artificially increasing ratings?
Credibility (Subjective – Cji, Objective – Ci)
Can we trust the ratings?
Transaction Factor (wlij)
Is the transaction relevant?

14. Estimating Reputation (cont.)
Relationship Factor (Fij)
How closely are the nodes connected
Centrality (Subjective – Rij, Objective – Ri)
How important is the node in the larger network?
Computation Capability (Ij)
How likely is it that the node will cheat?

15. Subjective Trustworthiness Model

16. Subjective Trustworthiness
The trustworthiness of pj as seen by pi is:
– centrality of pj in pi’s “life”
– pi’s direct experience with pj
– Experience of pi and pj’s common friends (Kij)
and are weights (total weight must be 1)

17. Calculating Centrality
– the set of common friends between pi and pj
– the set of neighbors of pi
Essentially, a ratio of common friends to neighbors.
Focuses centrality on the neighborhood, rather than the entire network

18. Calculating Direct Opinion
Long Term
Opinion
Recent
Opinion
Weighting of
Transaction
History
Weight
Weighting of
Most Recent
Observations
Weight
Relationship
Factor
Computation
Capability

19. Remembering Opinions The lengths of long and short term windows
Transaction weight factor
Transaction feedback

20. Calculating Indirect Opinions
Sums the credibility of all of the K peers’ direct opinions where
Factors are weighted between peers’ direct opinions and centrality

21. Quantifying Trustworthiness
The trustworthiness of pj as seen by pi is:
And remembering asymmetry, we know that
This only works if pi have a direct social relationship (neighbors)

22. Trustworthiness for non-Neighbors
The product of Trustworthiness of all nodes along the path to the service provider

23. Providing Feedback to Neighbors
If peer node was correct in its advice, then its opinion is reinforced
Feedback on neighbors is stored locally and used for future trust evaluations

24. Objective Trustworthiness Model

25. Trustworthiness Storage
Trustworthiness is stored in a DHT which is accessible by all nodes
Only Pre-Trusted Objects are permitted to store data in the DHT

26. Objective Trustworthiness
The objective model removes direct and indirect opinions

27. Calculating Centrality
Qj – The number of times pj requested a service
Aj – The number of times pj acted as an intermediate node in a transaction
Hj – The number of times pj provided a service
A node is central if it is involved in many transactions (not just as a requester)
Centrality is now network wide

28. Remembering Opinions Transaction weight factor
Considers feedback from all
Nodes that interacted with pj
Transaction feedback
Credibility

29. Objective Credibility
Considers Trustworthiness (Ti), Relationships (Fij), Intelligence (Ij), and Number of Transactions (Nij)
Higher intelligence, stronger relationships, and many transactions are assumed to be indicators for collusive malicious behavior

30. Experimental Evaluation

31. Simulation Setup
Small World In Motion model and a Brightkite social network dataset
Each human owns a set of things connected to the SIoT. ½ of their things are with them as they move
Nodes may be benevolent and cooperative or malicious (depending on relationship and intelligence)

32. Simulation Parameters (and optimal configuration)

33. Varying Thing Types (SWIM)
Malicious Nodes are only
Class 2 (Sensor/RFID)
Malicious Nodes are only
Class 1 (Intelligent Devices)

34. Varying Thing Types (BrightKite)
Malicious Nodes are only
Class 2 (Sensor/RFID)
Malicious Nodes are only
Class 1 (Intelligent Devices)

35. Varying the Percentage of Malicious Nodes (BrightKite)
Subjective Trustworthiness Model
Objective Trustworthiness Model

36. Dynamic Behavior Milking Reputation Building Reputation Oscillating

37. Summary Subjective model has a slower response to changes
Subjective model is immune to malicious actors who vary their behavior based on relationships

38. Questions?