This research sets out to investigate how Multi-Party Computation can be integrated into a data warehouse system to enable secure, privacy-preserving analyses and demonstrates that such a system can be viable, if it accounts well for performance issues of the technology.
This research offers a pragmatic blueprint for digital transformation in resource-constrained settings, contributing to the discourse on leveraging data architectures for improved public service delivery and evidence-based policymaking.
The safe system described in this paper tackles healthcare analytics problems by combining blockchain technology, privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), and a multi-tenant cloud environment.
Umme Habeeba Fatima, Lubna Nausheen, Sadaf Jahan· American Journal of AI Cyber...· 0 citations
This work expands upon the privacy threat assessment model to quantitatively evaluate the risks of data likability, identifiability, non-repudiation, detectability, unintended disclosure, indulgence, and policy & consent noncompliance, and constructs a framework aimed at mitigating these identified risks.
Jamila Alsayed Kassem, Tim Müller, Christopher A. Esterhuyse et al.· 0 citations
Modern organizational analytics rely on enterprise data warehouses (EDWs). However, large-scale and centralized AI-driven mining of sensitive data stored in these warehouses means that an organization is vulnerable to privacy leaks, inference attacks, and not complying with regulations. Many of the current privacy-pres...
Sangeetha S. B., T. C.· International Journal of Dat...· 0 citations
Organizations operating regulated database workloads across two or more public clouds should prioritize federated identity management, policy-as-code security baselines, centralized telemetry, and automated audit-evidence generation before expanding provider-specific security tooling.
Sai Vamsi Krishna Vadlamudi· American Journal of Technolo...· 0 citations
A security-by-design reference architecture for data governance in healthcare Digital Twins is proposed, derived through a combined research-driven and threat-driven methodology, explicitly linking requirements to data lifecycle threats and to relevant standards.
C. Braghin, S. Cimato, Andrea Marchesini et al.· International Conference on...· 0 citations
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