Several concerns are raised due to the widespread technology of Internet of Things and big data, which possess private and protection of information. Several researchers have analyzed different privacy preserving techniques, which still cannot provide equal stability between the data privacy and the utility and improvement in the scalability and efficiency. Data mining is one of the prominent technologies, which extracts reliable and useful knowledge from vast amount of information. Henceforth, mining of data stream have become a most popular and important research issue. Due to fast growth in the data generation, the mechanism of privacy preserving with high utility and security becomes more necessary. In this research, an improved efficient perturbation method for data stream named privacy-preserving rotation-based condensation algorithm with geometric transformation is proposed that delivers high data utility when compared with other existing techniques. This improved method gives high resilience against the attacks during the process of data reconstruction. Simulation result shows that the proposed method can acquire data privacy and improves accuracy during mining of data streams in which the analysis is performed for different datasets in which the proposed technique obtains more than 95% when compared with original dataset. © 2020 John Wiley & Sons, Ltd.