Privacy-Preserving Cross-Disciplinary Design Innovation: A Data Reprogramming Approach

Abstract

In the era of data-driven innovation, interdisciplinary design increasingly leverages vast datasets to inform and optimize creative processes. However, the integration of diverse data often introduces significant challenges related to data privacy and security. This paper proposes a novel framework for privacy-preserving cross-disciplinary design innovation, inspired by the principles of data reprogramming. We aim to develop a methodology that enables the effective utilization of sensitive data in design contexts while rigorously safeguarding individual privacy. Building on generative modeling principles, our approach improves data representation by balancing privacy preservation and feature robustness. We demonstrate how information bottleneck theory and reinforcement learning can be integrated to balance predictive power with privacy preservation, ensuring that design insights are derived from data without compromising sensitive information. This framework is particularly relevant for applications in healthcare, smart cities, and personalized education, where design solutions must be both innovative and ethically sound. Through extensive experimentation and validation, we show that our method achieves strong performance in design-related predictive tasks while effectively mitigating privacy risks, thereby paving the way for a new generation of privacy-conscious design methodologies.