The prevalence of mobile devices led the authorities to collect enormous volume of spatio-temporal trajectories. This practice may lead to the exposition of valuable sensitive personal details to adversaries and thereby privacy may be endangered. But the publication of datasets is essential for developmental activities. Hence anonymization of trajectory before publishing is imperative. In this work, instead of anonymizing the whole trajectory, it focuses on some stay locations on the trajectory as most sensitive to the user and anonymized them to provide privacy. This work suggests a perfect blend of existing generalization techniques with Places of Interests along with a new temporal perturbation technique for the anonymization of sensitive stay locations by adding temporal noise values. The experiments and evaluations with real-world datasets prove that this approach reduces unnecessary anonymization of trajectories and provide high data utility, less information loss and greater privacy for the users during the trajectory publication.