UBNB-PPDP: Utility-Boosting Negotiation-Based Privacy Preserving Data Publishing

Abstract

In the era of big data and artificial intelligence, almost every aspect of research is driven by collecting data. However, privacy concerns substantially limit the usability of such data. This prevents a vast amount of possible advances in all branches of science. While privacy rules are inevitable, data owners will always seek data publishing models and techniques that can maximize data utility within the frame of the imposed privacy rules. In this paper we propose a negotiation-based data publishing model to jointly address the utility requirements of the Data User (DU) and the privacy and possibly the monetary requirements of the Data Owner (DO). We also re-define the data utility based on the DU's rather than the DO's perspective. Based on the proposed model, we present two data publishing scenarios that satisfy a given privacy constraint while achieving the DU's required data utility. The variation in a DO's flat or variable monetary rate objective motivates the data publishing scenarios. Our protocol fills the gap between the existing theoretical work and the ultimate goal of practicality.