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.
Healthcare analytics presents a big difficulty in protecting sensitive data while yet offering insightful information because of the sensitivity of personal health information and the increasing frequency of data breaches. The safe system described in this paper tackles these problems by combining blockchain technology, privacy-preserving parameters, zero-knowledge proofs (zk-SNARKs), and a multi-tenant cloud environment. The system uses state-of-the-art cryptographic techniques, specifically zk-SNARKs, to guarantee that healthcare records are safeguarded during analytics computations without revealing raw data. The privacy-preserving analytics engine uses anonymised medical records and creates zk-SNARKs to verify calculations. These proofs create a visible, impenetrable ledger that ensures secure healthcare transactions when integrated into a blockchain network. In situations like telemedicine, when secure data sharing and processing are crucial, this approach is imperative. The framework's practical value in healthcare analytics is demonstrated by its deployment in a telemedicine app, which offers a scalable and secure solution to an urgent problem.
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-preserving frameworks are not designed for direct integration with OLAP environments. As a result, they cannot provide both high analytical utility and formal privacy protection. The framework proposed in this paper provides a definitive solution through integrating OLAP-based multidimensional feature engineering and federated learning (FL) through FedAvg and differentially private stochastic gradient descent (DP-SGD). The system is designed to partition EDW data across multiple logical clients and allows for collaborative training of a single global neural network while keeping all raw data stored locally within each client. Accuracy of classification results for the UCI Adult Income public benchmark and a synthetic EDW-based dataset tested against four baseline models are reported to be 94.8% with a ε of 2.0 for the synthetic EDW dataset, which is only 1.4% lower than centralized. Furthermore, the use of warehouse-aware federated analytics methods has been shown to reduce membership inference attack (MIA) success rates from 79.4% to 54.8%. Finally, the overall system was maintained with a formal (ε, δ)-differential privacy guarantee, thus validating the solution as being scalable, compliant with regulations, and suitable for the development of privacy-preserving enterprise AI.
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
: Digital Twins (DTs) are increasingly adopted in healthcare to enable real-time patient monitoring, predictive analytics, and decision support. However, their deployment relies on highly distributed and heterogeneous infrastructures, where sensitive medical data are continuously exchanged across multiple components. This raises critical challenges for secure data governance, including the protection of data across its lifecycle, compliance with regulatory frameworks, and interoperability among existing systems. Despite the growing body of research on healthcare DTs, existing approaches lack a systematic integration of security, privacy, and compliance mechanisms within a unified architectural model. In particular, no reference architecture currently addresses security concerns across data in transit, at rest, and in use in a comprehensive and reusable manner. In this paper, we address this gap by proposing a security-by-design reference architecture for data governance in healthcare Digital Twins. The architecture is derived through a combined research-driven and threat-driven methodology, explicitly linking requirements to data lifecycle threats and to relevant standards (e.g., GDPR, HIPAA, ISO). To assess feasibility, we implement a prototype based on a microservices architecture and evaluate it in a representative healthcare scenario. The results show how the proposed solution supports secure data management across data in transit, at rest, and in use, while preserving interoperability and compliance. It can thus be considered as a practical and reusable foundation for secure data governance in healthcare DTs.
C. Braghin, S. Cimato, Andrea Marchesini et al.· International Conference on...· 0 citations
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