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Trustworthiness Management in the Social Internet of Things

Trustworthiness Management in the Social Internet of Things. Michele Nitti, Roberto Girau , and Luigi Atzori Presented by: Chris Morrell, 24 April 2014. Agenda. Introduction & Background The Proposed Solution The Subjective Trust Model The Objective Trust Model Experimental Results

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Trustworthiness Management in the Social Internet of Things

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  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 • SIoTArchitecture • 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. The Proposed Solution Objects mimic human social behavior

  12. Trust Models • Subjective 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 Computation Capability Relationship Factor

  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 pjprovided 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 relationshipand 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 Reputation

  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?

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