[Vision Paper] Privacy-Preserving Data Integration
Lisa Trigiante, Domenico Beneventano, Sonia Bergamaschi · 2023
The digital transformation of different processes and the resulting availability of vast amounts of data describing people and their behaviors offer significant promise to advance multiple research areas and enhance both the public and private sectors. Exploiting the full potential of this vision requires a unified representation of different autonomous data sources to facilitate detailed data analysis capacity. Collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. This vision paper provides a concise overview of the research field concerning Privacy-Preserving Data Integration (PPDI), the associated challenges, opportunities, and unexplored aspects, with the primary aim of designing a novel and comprehensive PPDI framework based on a Trusted Third-Party microservices architecture.