Granular data representation under privacy protection: Tradeoff between data utility and privacy via information granularity

Abstract

Information granules describe available experimental evidence at a more abstract level and facilitate the concise characterization of the structure of the numeric data. The utilization of unreliable data collectors poses a key challenge with respect to the privacy of the participants. In order to guarantee data confidentiality, we propose a granular data publishing method under differential privacy. In this study, a two-stage design of information granules considering the requirement of data privacy is proposed. The main idea is to protect individual sensitive pieces of data information against inference attacks and optimize the representation capabilities of information granules in a multi-dimensional data space. In the first stage, a clustering algorithm based on differential privacy is proposed to find some numeric prototypes in a numeric space, which considers sensitive data privacy protection. Second, this paper constructs spherical information granules involving the requirement of differential privacy. Their multi-dimensional and fuzzy granularity characteristics are considered. An improvement of the principle of justifiable granularity is utilized to optimize the bounds of information granules, and a new objective function is established to optimize the information granules. Our work is devoted to tradeoff data privacy and utility by constructing reasonable information granules. Unlike numerical data publishing, the publishing of interval information granules improves data utility in data protection. Experimental results demonstrate the superiority of the proposed granular description method based on differential privacy.