Privacy preservation in wireless networks is a multidomain task, including encryption, hashing, secure routing, obfuscation, and third-party data sharing. To design a privacy preservation model for wireless networks, it is recommended that data privacy, location privacy, temporal privacy, node privacy, and route privacy be incorporated. However, incorporating these models into any wireless network is computationally complex. Moreover, it affects the quality of services (QoS) parameters like end-to-end delay, throughput, energy consumption, and packet delivery ratio. Therefore, network designers are expected to use the most optimum privacy models that should minimally affect these QoS metrics. To do this, designers opt for standard privacy models for securing wireless networks without considering their interconnectivity and interface-ability constraints. Due to this, network security increases, but overall, network QoS is reduced. To reduce the probability of such scenarios, this text analyses and reviews various state-of-the-art models for incorporating privacy preservation in wireless networks without compromising their QoS performance. These models are compared on privacy strength, end-to-end delay, energy consumption, and network throughput. The comparison will assist network designers and researchers to select the best models for their given deployments, thereby assisting in privacy improvement while maintaining high QoS performance.Moreover, this text also recommends various methods to work together to improve their performance. This text also recommends various proven machine learning architectures that can be contemplated & explored by networks to enhance their privacy performance. The paper intends to provide a brief survey of different types of Privacy models and their comparison, which can benefit the readers in choosing a privacy model for their use.