Trustworthiness Management in the Social Internet of Things Michele Nitti, Roberto Girau, and Luigi Atzori

Outline Introduction Background The Proposed Solution Trust Models Basic Trust Elements Subjective and Objective Models Subjective Trustworthiness Objective Trustworthiness Experimental Evaluation Conclusion

Social IoTs and Trust Management Integrate the social network concepts into the Internet of things Use social networking elements in the Internet of things to allow objects to autonomously establish social relationships. The goal is to understand how the objects in the SIoTs process the information and build a reliable system based on the behaviors of objects. Object relationships: Parental object relationships co-location and co-work object relationships Ownership object relationships Social object relationships

Social IoTs and Trust Management Four major components of Social IoT architecture: Relationship management service discovery service composition trustworthiness management The major contribution of this work: Define the problem of trustworthiness management in the SIoT. Define two models for trustworthiness management (subjective and objective) starting from the solutions proposed for social networks and P2P. Evaluation of the benefits of the trustworthiness management in the IoT.

State of the Art in P2P Networks Trust Management To calculate the peer trustworthiness, the system has to store the reputation information.

State of the Art in Social Networks Trust Management Transitivity: It is based on the concept of recommendation of someone that is not directly known. A trusts B, B trusts C, then A trusts C. Composability: Compose the recommendations from different friends into a unique value and then decide whether to trust someone or not. Personalization: Trust is related to a person’s past experience. Asymmetric: Two people tied a relationship may have different levels of trustworthiness to each other.

Notation and Problem Definition Node 𝒫= 𝑝 1 , 𝑝 2 …, 𝑝 𝑀 Service providers Service requesters Components: 𝒩 1 ={ 𝑝 2 , 𝑝 3 , 𝑝 4 } 𝒦 1,4 ={ 𝑝 2 , 𝑝 3 } Service discovery 𝒵 5 ={ 𝑝 10 } ℛ 1,5 ={ 𝑝 1 𝑝 4 , 𝑝 4 𝑝 8 , 𝑝 8 𝑝 5 }

Subjective Trustworthiness From social point of view. Each node calculate the trustworthiness of its neighbors based on its experience and that of its friends. If two nodes are not friends, the trustworthiness is calculated by word of mouth through a chain of friends. Transitivity: All the objects in the SIoT try to achieve the same goal. Composability: Combine the recommendations from 𝒦 𝑖,𝑗 . Personalization and Asymmetry: personal experience and opinion.

Objective Trustworthiness From P2P scenarios. The information of each node is distributed and stored by a Distributed Hash Table (DHT) structure. The information is visible to every node but is only managed by special nodes, named Pre-trusted objects. The experience of each node is shared with the entire network, so there is no transitivity, personalization, and asymmetry. Composability: Combine the past works, concepts of weighted feedback and credibility to estimate trust values.

Basic Trust Elements Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Subjective Trustworthiness Assume 𝑝 𝑖 and 𝑝 𝑗 are adjacent nodes: 𝑇 𝑖𝑗 = 1−𝛼−𝛽 𝑅 𝑖𝑗 +𝛼 𝑂 𝑖𝑗 𝑑𝑖𝑟 +𝛽 𝑂 𝑖𝑗 𝑖𝑛𝑑 The centrality of 𝑝 𝑗 with respect to 𝑝 𝑖 : 𝑅 𝑖𝑗 = 𝒦 𝑖𝑗 /( 𝒩 𝑖 −1) When 𝑝 𝑖 needs the trustworthiness of 𝑝 𝑗 , it checks the last direct transactions and determines its own opinion: 𝑂 𝑖𝑗 𝑑𝑖𝑟 = log 𝑁 𝑖𝑗 +1 1+ log 𝑁 𝑖𝑗 +1 𝛾 𝑂 𝑖𝑗 𝑙𝑜𝑛 + 1−𝛾 𝑂 𝑖𝑗 𝑟𝑒𝑐 + 1 1+ log 𝑁 𝑖𝑗 +1 (𝛿 𝐹 𝑖𝑗 +(1−𝛿)(1− 𝐼 𝑗 )) Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Subjective Trustworthiness The long and short-term opinions: 𝑂 𝑖𝑗 𝑙𝑜𝑛 = 𝑙=1 𝐿 𝑙𝑜𝑛 𝜔 𝑖𝑗 𝑙 𝑓 𝑖𝑗 𝑙 𝑙=1 𝐿 𝑙𝑜𝑛 𝜔 𝑖𝑗 𝑙 𝑂 𝑖𝑗 𝑟𝑒𝑐 = 𝑙=1 𝐿 𝑟𝑒𝑐 𝜔 𝑖𝑗 𝑙 𝑓 𝑖𝑗 𝑙 𝑙=1 𝐿 𝑟𝑒𝑐 𝜔 𝑖𝑗 𝑙 The indirect opinion 𝑂 𝑖𝑗 𝑖𝑛𝑑 = 𝑘=1 𝒦 𝑖𝑗 ( 𝐶 𝑖𝑘 𝑂 𝑘𝑗 𝑑𝑖𝑟 ) / 𝑘=1 𝒦 𝑖𝑗 𝐶 𝑖𝑘 The credibility value: 𝐶 𝑖𝑘 =𝜂 𝑂 𝑖𝑘 𝑑𝑖𝑟 + 1−𝜂 𝑅 𝑖𝑘 Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Subjective Trustworthiness Assume 𝑝 𝑖 and 𝑝 𝑗 are not adjacent (not linked by a direct social relationship): 𝑇 𝑖𝑗 ′ = 𝑎,𝑏: 𝑝 𝑖𝑗 𝑎 𝑝 𝑖𝑗 𝑏 ∈ ℛ 𝑖𝑗 𝑇 𝑎𝑏 The feedback generated by 𝑝 𝑖 is stored locally and used for future trust evaluations: 𝑓 𝑖𝑘 𝑙 = 𝑓 𝑖𝑗 𝑙 𝑖𝑓 𝑂 𝑘𝑗 𝑑𝑖𝑟 ≥0.5 1− 𝑓 𝑖𝑗 𝑙 𝑖𝑓 𝑂 𝑘𝑗 𝑑𝑖𝑟 <0.5 Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Subjective Trustworthiness

Objective Trustworthiness The values used to compute the trustworthiness are stored in Distributed Harsh Table (DHT) (CAN, Chord, Pastry). A DHT system is based on an abstract key space. Each node is responsible for a set of keys. An overlay network connects the nodes, allowing them to find the owner of any given key in the key space. To store a file with a given filename and data, a key for the filename is generated through a hash function and the data and the key are sent to the node responsible for that key. If a node wants retrieve the data, it first generates the key from the filename and then sends to the DHT a request for the node that holds the data with that key. In this paper, every node can query the DHT to retrieve the trustworthiness value of every other node in the network.

Objective Trustworthiness The trustworthiness: 𝑇 𝑗 = 1−𝛼−𝛽 𝑅 𝑗 +𝛼 𝑂 𝑗 𝑙𝑜𝑛 +𝛽 𝑂 𝑗 𝑟𝑒𝑐 Centrality: 𝑅 𝑗 = 𝐴 𝑗 + 𝐻 𝑗 𝑄 𝑗 + 𝐴 𝑗 + 𝐻 𝑗 The short and long term opinions: 𝑂 𝑗 𝑙𝑜𝑛 = 𝑖=1 𝑀 𝑙=1 𝐿 𝑙𝑜𝑛 𝐶 𝑖𝑗 𝜔 𝑖𝑗 𝑙 𝑓 𝑖𝑗 𝑙 / 𝑖=1 𝑀 𝑙=1 𝐿 𝑙𝑜𝑛 𝐶 𝑖𝑗 𝜔 𝑖𝑗 𝑙 𝑂 𝑗 𝑟𝑒𝑐 = 𝑖=1 𝑀 𝑙=1 𝐿 𝑟𝑒𝑐 𝐶 𝑖𝑗 𝜔 𝑖𝑗 𝑙 𝑓 𝑖𝑗 𝑙 / 𝑖=1 𝑀 𝑙=1 𝐿 𝑟𝑒𝑐 𝐶 𝑖𝑗 𝜔 𝑖𝑗 𝑙 Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Objective Trustworthiness The credibility: 𝐶 𝑖𝑗 = 1−𝛾−𝛿 𝑇 𝑖 +𝛾 1− 𝐹 𝑖𝑗 +𝛿(1− 𝐼 𝑗 ) 1+ log ( 𝑁 𝑖𝑗 +1) Feedback system 𝑓 𝑖𝑗 𝑙 Total number of transactions 𝑁 𝑖𝑗 Credibility 𝐶 𝑖𝑗 Transaction factor 𝜔 𝑖𝑗 𝑙 Relationship factor 𝐹 𝑖𝑗 Notion of centrality 𝑅 𝑖𝑗 Computation capability 𝐼 𝑗

Experimental Evaluation Data: Synthetic data: generated by mobility model called Small World In Motion (SWIM). Real dataset of the location-based online social network Brightlite. Two different behaviors considered in a social network: social nodes and malicious nodes.

Malicious Node Behavior

Parameter Setting Transaction success rate when the system has reached the steady- state using the SWIM data.

Transaction Success Rate

Transaction Success Rate

Dynamic Behavior

Dynamic Behavior

Conclusion This paper focuses on the trustworthiness management in the social IoT by proposing subjective and objective approaches. Subjective approach: Slower transitory response, especially when dealing with nodes with dynamic behaviors. Immune to behaviors typical of social networks Objective approach: Suffers from behavior of social networks, since a node’s trustworthiness is global for the entire network Including both the opinion from malicious nodes and social nodes.

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