Privacy Passport: Privacy-Preserving Cross-Domain Data Sharing

Data sharing facilitates the integration and in-depth exploration of cross-domain data, thereby fostering innovative research and model development. However, privacy leakage emerges as a critical barrier to the sharing and circulating of such data. Existing privacy-preserving technologies face challenges in handling complex scenarios involving multiple participants due to the following reasons: 1) Divergent privacy permission. Data sharing is constrained by various privacy limitations, necessitating the consideration of privacy permissions across different domains, akin to a cross-border process. 2) High collaboration cost. Collaboration among multiple domains to determine the privacy constraint and sharing ways incur additional costs. 3) Large noise magnitude. Traditional privacy techniques to protect the privacy of a single domain using local differential privacy (LDP) may introduce excessive noise, thereby reducing data utility. Drawing inspiration from the cross-border visa issuance process, we present an innovative framework called PriVisa for enabling privacy-preserving data sharing across different domains. It consists of four key modules to overcome the mentioned challenges: the hybrid pattern, optimized sharing path construction, personalized grouping, and LDP-based perturbation. 1) The hybrid pattern for coordination among organizations, considering authentication, privacy constraints, and sharing methods. 2) The optimized sharing path construction using a privacy constraint hierarchy tree to maximize data utility while adhering to privacy requirements. 3) The feature similarity grouping and perturbing mechanism satisfying LDP to protect privacy and optimize data utility. The theoretical and experimental validation confirms PriVisa's effectiveness in addressing divergent privacy constraints and promoting data utility in cross-domain data sharing.