Trustworthiness Management in the Social Internet of ThingsMichele 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• Conclusion

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.

Purpose

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

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

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

Properties of Trustworthiness

• Transitivity: Depends on the purpose• Composability: Combining recommendations• Personalization: Differing opinions matter• Asymmetric: See personalization

Notation• The set of nodes:

• An undirected graph describing the network:

• A node i’s neighborhood

• Common friends between pi and pj

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

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)

The Proposed Solution

Objects mimic human social behavior

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)

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?

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?

Subjective Trustworthiness Model

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)

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

Calculating Direct OpinionWeighting ofTransactionHistory

Long TermOpinion

RecentOpinion

Weight

RelationshipFactor

ComputationCapability

WeightWeighting ofMost RecentObservations

Remembering Opinions

The lengths of long and short term windows

Transaction weight factor

Transaction feedback

Calculating Indirect Opinions

• Sums the credibility of all of the K peers’ direct opinions where

• Factors are weighted between peers’ direct opinions and centrality

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)

Trustworthiness for non-Neighbors

• The product of Trustworthiness of all nodes along the path to the service provider

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

Objective Trustworthiness Model

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

Objective Trustworthiness

• The objective model removes direct and indirect opinions

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

Remembering Opinions

Transaction weight factor

Transaction feedback

Credibility

Considers feedback from allNodes that interacted with pj

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

Experimental Evaluation

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)

Simulation Parameters(and optimal configuration)

Varying Thing Types (SWIM)

Malicious Nodes are onlyClass 2 (Sensor/RFID)

Malicious Nodes are onlyClass 1 (Intelligent Devices)

Varying Thing Types (BrightKite)

Malicious Nodes are onlyClass 2 (Sensor/RFID)

Malicious Nodes are onlyClass 1 (Intelligent Devices)

Varying the Percentage of Malicious Nodes (BrightKite)

Subjective Trustworthiness Model Objective Trustworthiness Model

Dynamic Behavior

MilkingReputation

BuildingReputation

OscillatingReputation

Summary

• Subjective model has a slower response to changes

• Subjective model is immune to malicious actors who vary their behavior based on relationships

Questions?