Università degli studi di Modena e Reggio Emilia

The digital transformation of health processes has resulted in the collection of vast amounts of health-related data that presents significant potential to support medical research projects and improve the healthcare system. Many of these possibilities arise as a consequence of integrating data from different sources to create an accurate and unified representation of the underlying data and enable detailed data analysis that is not possible through any individual source. Achieving this vision requires the collection and processing of sensitive health-related data about individuals, thus privacy and confidentiality implications have to be considered. In this paper, I describe my doctoral research topic: the design and development of a novel Privacy-Preserving Data Integration (PPDI) framework which aims to effectively address the challenges and opportunities of integrating Big Health Data (BHD) while ensuring compliance with the General Data Protection Regulation (GDPR). The paper describes the planned methodology for implementing the PPDI process through the usage of data pseudonymization techniques and Privacy-Preserving Record Linkage (PPRL) methods and provides an overview of the new framework, which is based on the re-implementation of MOMIS towards a microservices architecture with added PPDI functionalities.

The digital transformation of the Justice domain and the resulting availability of vast amounts of data describing people and their criminal behaviors offer significant promise to feed multiple research areas and enhance the criminal justice system. Achieving this vision requires the integration of different sources to create an accurate and unified representation that enables detailed and extensive data analysis. However, the collection and processing of sensitive legal-related data about individuals imposes consideration of privacy legislation and confidentiality implications. This paper presents the lesson learned from the design and develop of a Privacy-Preserving Data Integration (PPDI) architecture and process to address the challenges and opportunities of integrating personal data belonging to criminal and court sources within the Italian Justice Domain in compliance with GDPR.

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 fa- cilitate detailed data analysis capacity. Collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. This vision paper pro- vides 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.