The safety of cloud services within a Trust Model System (TMS) is certainly compromised by a lack of defense against security threats as well as by inaccuracy of the trust results. Our proposed model addresses well-known security threats to the reputation trust model system, and is shown to deal with all possible potential attack threats, such as Sybil, on–off, and collusion attacks, by specifying the identity of users and tracking activities undertaken by them in order to easily track unauthorized consumers or attackers and to provide proof of any kind of data leakage. The TMS can also oversee the authorization of whoever uploads feedback into the system. It can also identify invalid feedback and discard it from the system. The algorithms of the TMS first establish a variety of trust criteria in which trustworthiness is calculated. Then, feedback from the cloud service provider nodes is accepted only according to the rules of the TMS. A consumer’s trust value is finally computed using a flexible system capable of guaranteeing a good balance of consumer trust and owners' feedback. Furthermore, a majority of the existing TMS models do not take full account of interaction importance, thus impeding the accuracy of the trust values, a shortcoming that has been rectified in our proposed model.

信任模型系统 (TMS) 中云服务的安全性肯定会因缺乏针对安全威胁的防御以及信任结果的不准确而受到损害。我们提出的模型解决了对声誉信任模型系统的众所周知的安全威胁，并通过指定用户的身份和进行的跟踪活动来处理所有可能的潜在攻击威胁，例如 Sybil、开-关和共谋攻击以便轻松跟踪未经授权的消费者或攻击者，并提供任何类型数据泄露的证据。TMS 还可以监督将反馈上传到系统的任何人的授权。它还可以识别无效反馈并将其从系统中丢弃。TMS 的算法首先建立各种信任标准，在这些标准中计算可信度。然后，仅根据 TMS 的规则接受来自云服务提供商节点的反馈。最终使用能够保证消费者信任和所有者反馈之间良好平衡的灵活系统来计算消费者的信任值。此外，大多数现有的 TMS 模型没有充分考虑交互重要性，从而阻碍了信任值的准确性，这是我们提出的模型中已纠正的缺点。