Privacy preservation of the user data and properly balancing between privacy and utility

The privacy and utility are the trade-off factors, where the performance of one factor should sacrifice to achieve the other. If privacy is achieved without publishing the data, then efficient utility cannot be achieved, hence the original dataset tends to get published without privacy. Therefore, it is essential to maintain the equilibrium between privacy and utility of datasets. In this paper, we propose a new privacy utility method, where the privacy is maintained by lightweight elliptical curve cryptography (ECC), and utility is maintained through ant colony optimisation (ACO) clustering. Initially, the datasets are clustered using ACO and then the privacy of clustered datasets is maintained using ECC. The proposed method has experimented over medical datasets and it is compared with existing methods through several performance metrics like clustering accuracy, F-measure, data utility, and privacy metrics. The analysis shows that the proposed method obtains improved privacy preservation using the clustering algorithm than existing methods. © 2022 Inderscience Enterprises Ltd.