# -- Select and Attempt task -- ```
## 4 Enhancing Performance by Avoiding Unwarranted Task Executions
### The Proposed CA Algorithm for the Early Detection of Bad Providers
It is essential to ensure that a trust model does not excessively consume a device's resources [21]. Both computational and storage overhead must be considered [31], as devices typically have constrained processing and storage capabilities and must prioritize their primary tasks over trust evaluation. Additionally, excessive computational overhead can hinder real-time trust assessments, negatively impacting time-sensitive applications. A trust model's efficiency can be analyzed using big O notation for time and space complexity. The CA model is considered simple and lightweight since its algorithm avoids complex mathematical computations. We provide a sketch of a computational complexity analysis of Algorithm 2 in Appendix A.2, which outlines the core reasoning behind the CA model's efficiency using big O notation. However, a detailed analysis (full formal proof) of its time and space complexity in big O notation remains to be conducted. Since all trust information is stored locally by the trustees, further research is needed to evaluate the CA model's storage overhead and explore potential optimizations to minimize it. **Accuracy**. A trust model must effectively identify and prevent malicious entities by ensuring precise classification [26]. It should achieve a high level of accuracy, meaning that the computed trust value closely reflects the true value (ground truth) [32]. Several studies [1; 10; 37; 38] assess proposed trust models using metrics such as Precision, Recall, F1-score, accuracy, False Positive Rate (FPR), and True Positive Rate (TPR). However, the accuracy of the CA model has not yet been evaluated using these metrics. **Robustness**. Trust models must withstand both anomalous behavior caused by sensor malfunctions [36] and trust-related attacks from insider attackers who deliberately act maliciously for personal gain or to disrupt system performance [26; 39]. In heterogeneous networks, constant device connectivity and weak interoperability between network domains create numerous opportunities for malicious activities [31]. Traditional cryptographic and authentication methods are insufficient against insider attacks, where attackers possess valid cryptographic keys [36]. Existing trust models can only mitigate certain types of attacks, but none can fully defend against all threats [25]. Therefore, developing algorithms capable of detecting a broad range of malicious activity patterns is crucial [26]. In the following section, we examine common trust-related attacks from the literature and evaluate the CA model's effectiveness in countering them. **Privacy Preservation**. Ensuring a high level of privacy through data encryption is essential during trust assessment to prevent sensitive information leaks [34]. A trust model must safeguard both Identity Privacy (IP) and data privacy (DP) [31]. Specifically, feedback and interaction data must remain protected throughout all stages of data management, including collection, transmission, storage, fusion, and analysis. Additionally, identity details such as names and addresses should be shielded from unauthorized access, as linking trust information to real identities can lead to serious risks, such as Distributed Denial of Service (DDoS) Attacks [35]. In the CA approach, while entities generally do not exchange trust information (e.g., recommendations), interaction feedback is transmitted from the trustor to the trustee, allowing the trustee to assess and locally store its own trustworthiness in the form of connections. Therefore, protecting the trustor's identity and ensuring data privacy during transmission, storage, and analysis are critical considerations in the CA approach. Exploring the integration of blockchain technology to enhance privacy protection could be a valuable future research direction. **Explainability**. Trust models should be capable of providing clear and understandable explanations for their results. The ability to analyze and justify decisions is essential for enhancing user trust, compliance, and acceptance [40].