# CATSWoTS: Context Aware Trustworthy Social Web of Things System
## 2 Background Study
### Trust Management Systems
In addition, the interoperability of heterogeneous devices was also not considered in this research. In [26], a contextual social network model was presented that considered independent social contexts such as role impact factor and preference, and dependent social contexts such as trust and social intimacy degree. A probabilistic approach was presented for social contextual-aware trust interference. An iterative approach was proposed in this research work for contextual trust interfaces to consider cycles along with strong and weak connections. The proposed system provided a solution to overcome data sparsity problems and getting recommendations from unknown participants. The results of the experiments showed that the proposed model yield positive and trustworthy results. However, QoS and interoperability were not able to get the attention of the researchers. In [27], the context-aware trust-based framework was proposed for the dissemination of information in the vehicular network. The proposed framework called FACT was composed of two modules: (a) admission module and (b) dissemination module. It first checked the authenticity and validity of the message sent. After checking stored trust values, it selected the best path for disseminating the message. The results of the simulation showed that FACT was more effective and scalable than other routing protocols like VADD. Another research work [28] focusing on privacy preserving data security and mining presented a framework called PPSF (Privacy Preserving and Security Mining Framework). This framework was an open-source library offering various algorithms for Privacy Preserving Utility Mining (PPUM), Privacy Preserving Data Mining (PPDM), and data mining. The framework showed the results after running the algorithm. In [29], a distributed trust model was proposed that considered the historical data of the nodes. Trust was calculated for a service seeker through a single function. However, interoperability and context awareness were not part of this study. Another trust evaluation model focusing on social IoT and context awareness was presented in [30]. The proposed model maintained quantified trustworthiness of a node in a distributed approach. The attributes such as context importance, energy, computation power and feedback of a service consumer were used as criteria for calculating the trustworthiness. However, it was identified that QoS based evaluation was ignored in the proposed model. A flexible trust-aware access control system for IoT (TACIoT) was proposed. It provided reliable, secure and end to end mechanism for this purpose [31]. To compute the value of trust about IoT devices, a trust model considered four aspects: (a) reputation, (b) security aspects (c) quality of service and (d) social relationships. The trust model was implemented using fuzzy control systems. The feasibility of the proposed framework was tested by running the software on real scenarios for constrained devices and non-constrained devices; however, the context-awareness gets less attention of the researchers. [PERSON] et al. [32] presented a survey that discussed the features of IoT. The research also explored the effects of security and privacy including the threats. Moreover, the state-of-the-art solutions tackling the stated issues were also discussed. Based on this, the research identified the challenges faced by this area and then stated the opportunities. In [33], a sanitization approach for hiding confidential information by ensuring useful information still available was presented. For balancing four size effects, HCMPSO and particle swarm optimization framework were used. For experimental purposes, NSGA-II and cpGA2 DT approaches were used that demonstrated the security of published data in IoT in a way that the information was hidden after sanitization. A multiobjective algorithm based on NSGA-II algorithm was presented in [34] that had two strategies for hiding sensitive data. More than one solution were provided by the proposed algorithm.