Best-Practice-Based Framework for User-Centric Privacy-Preserving Solutions in Smart Home Environments

The rapid technological progress causes smart environments, such as smart homes, cities, etc., to become more ubiquitous in our daily lives. Privacy issues arise when the smart objects in those smart environments collect and disclose sensitive data without users’ consent. Therefore, existing works and the European General Data Protection Regulation (GDPR) are still calling for privacy-preserving solutions with more user involvement and automated decision-making. Existing works show research gaps regarding context-aware privacy-preference modellings. They do not present best-practice-based frameworks for user-centric privacy-preserving approaches allowing context-aware adapting of users’ privacy and data disclosure preferences while considering their past activities. Hence, this paper proposes a best-practice-based framework for user-centric privacy-preserving solutions with automation options. The proposed approach supplies users data sharing recommendations with minimum human interference while considering (1) GDPR requirements, (2) context-sensitive factors and (3) users’ past activities. The paper also outlines how the proposed framework can be integrated in an existing user-centric privacy-preserving approach in the future. In this way, the proposed approach can be integrated in the existing IoT architecture systems, which allow users to control the entire data collection, storage and disclosure process in smart home environments.