A hybrid approach for preserving privacy for real estate data

In the present digital world, usage of the internet has increased many folds as users have become dependent on the cloud-based applications. The disclosure of personal information on such platforms becomes a prospective threat for an attack. Researchers have used randomised data distortion technique with addition of random noise to conceal the sensitive data from an unauthorised adversary. This perturbation technique has relevance for the numerical datasets only. In this paper, we propose a hybrid model of two phases encoding with additive random noise value for ensuring non-disclosure of private and sensitive information and maintaining an effective balance between data privacy and data utility. The proposed technique has been tested on different data sizes of the real estate industry in terms of efficiency and effectiveness in preserving privacy and data utility. The proposed algorithm has been evaluated in terms of privacy level and information loss. It has proved effective in comparison with other privacy-preserving techniques such as perturbation and encryption in terms of space complexity and efficiency.