Assuring Trustworthy Data: A Dual-Criteria Analysis of Anonymization and System Reliability in Digital Health (A Systematic Review) - Inocêncio, Giovana Nunes; Martina, Jean Everson (Anais do Simpósio Brasileiro de Sistemas de Informação (SBSI); 2026: Anais do XXII Simpósio Brasileiro de Sistemas de InformaçãoDO - 10.5753/sbsi.2026.248772, 2026)
Research Context: The Digital Health sector faces the critical challenge of reconciling clinical data privacy with system reliability and utility. The risk in Brazil is high; the sector is the most targeted by ransomware, facing catastrophic financial losses. Scientific and/or Practical Problem: A persistent gap exists in the literature concerning the systematic evaluation of how anonymization techniques impact integrity and the lack of consensus on the dual criteria (technical and sociotechnical) necessary to assess effectiveness in Health Information Systems (HIS). Proposed Solution and/or Analysis: A Systematic Literature Review (SLR) mapped anonymization techniques (RQ1), analyzed their reliability impact (RQ1.1), and identified formal criteria for user trust (RQ2), addressing the core privacy-utility dilemma. Related IS Theory: The study is based on the Sociotechnical Systems Theory (SST), recognizing that anonymization and reliability are outcomes of integrating technology with critical social factors, including governance and user trust. The research aligns with GranDSI-BR Challenges 2 and 4. Research Method: The SLR followed Kitchenham and PRISMA guidelines, utilizing the PICOC model. The search across four major databases yielded 20 high-quality articles published between 2020 and 2025. Summary of Results: Dominant techniques are Blockchain and Federated Learning (FL), substantially enhancing data integrity and the privacy-utility balance. Reliability is dually assessed by Technical criteria (e.g., Re-identification Risk) and Sociotechnical criteria (e.g., Governance, Public Perception). Contributions and Impact to IS area: The study consolidates a theoretical and empirical framework on anonymization’s influence on reliability, meeting GranDSI-BR Challenges 2 and 4. It offers practical subsidies for managers and regulators in designing verifiable and trustworthy digital health systems.