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Special Issue Information

Dear Colleagues,

The rapid growth of data-driven technologies for large-scale data processing have raised significant concerns about data privacy and security. This Special Issue aims to explore these various challenges related to big data and address issues with advanced technologies. The scope of this Special Issue includes differential privacy, anonymization schemes for multi-dimensional data, studies on various threat models, and the human aspect of privacy policies for privacy technologies.

Prof. Dr. Hiroaki Kikuchi
Guest Editor

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Keywords

differential privacy
data anonymization
privacy policy
membership inference
attribute inference
surveillance camera

Published Papers (2 papers)

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Research

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23 pages, 1267 KiB

Open AccessArticle

Comparative Analysis of Local Differential Privacy Schemes in Healthcare Datasets

by Andres Hernandez-Matamoros and Hiroaki Kikuchi

Appl. Sci. 2024, 14(7), 2864; https://doi.org/10.3390/app14072864 - 28 Mar 2024

Viewed by 410

Abstract

In the rapidly evolving landscape of healthcare technology, the critical need for robust privacy safeguards is undeniable. Local Differential Privacy (LDP) offers a potential solution to address privacy concerns in data-rich industries. However, challenges such as the curse of dimensionality arise when dealing with multidimensional data. This is particularly pronounced in k-way joint probability estimation, where higher values of k lead to decreased accuracy. To overcome these challenges, we propose the integration of Bayesian Ridge Regression (BRR), known for its effectiveness in handling multicollinearity. Our approach demonstrates robustness, manifesting a noteworthy reduction in average variant distance when compared to baseline algorithms such as LOPUB and LOCOP. Additionally, we leverage the R-squared metric to highlight BRR’s advantages, illustrating its performance relative to LASSO, as LOPUB and LOCOP are based on it. This paper addresses a relevant concern related to datasets exhibiting high correlation between attributes, potentially allowing the extraction of information from one attribute to another. We convincingly show the superior performance of BRR over LOPUB and LOCOP across 15 datasets with varying average correlation attributes. Healthcare takes center stage in this collection of datasets. Moreover, the datasets explore diverse fields such as finance, travel, and social science. In summary, our proposed approach consistently outperforms the LOPUB and LOCOP algorithms, particularly when operating under smaller privacy budgets and with datasets characterized by lower average correlation attributes. This signifies the efficacy of Bayesian Ridge Regression in enhancing privacy safeguards in healthcare technology. Full article

(This article belongs to the Special Issue Data Privacy and Security for Information Engineering)

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Review

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47 pages, 650 KiB

Open AccessReview

Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare

by Steven M. Williamson and Victor Prybutok

Appl. Sci. 2024, 14(2), 675; https://doi.org/10.3390/app14020675 - 12 Jan 2024

Cited by 1 | Viewed by 6725

Abstract

Integrating Artificial Intelligence (AI) in healthcare represents a transformative shift with substantial potential for enhancing patient care. This paper critically examines this integration, confronting significant ethical, legal, and technological challenges, particularly in patient privacy, decision-making autonomy, and data integrity. A structured exploration of these issues focuses on Differential Privacy as a critical method for preserving patient confidentiality in AI-driven healthcare systems. We analyze the balance between privacy preservation and the practical utility of healthcare data, emphasizing the effectiveness of encryption, Differential Privacy, and mixed-model approaches. The paper navigates the complex ethical and legal frameworks essential for AI integration in healthcare. We comprehensively examine patient rights and the nuances of informed consent, along with the challenges of harmonizing advanced technologies like blockchain with the General Data Protection Regulation (GDPR). The issue of algorithmic bias in healthcare is also explored, underscoring the urgent need for effective bias detection and mitigation strategies to build patient trust. The evolving roles of decentralized data sharing, regulatory frameworks, and patient agency are discussed in depth. Advocating for an interdisciplinary, multi-stakeholder approach and responsive governance, the paper aims to align healthcare AI with ethical principles, prioritize patient-centered outcomes, and steer AI towards responsible and equitable enhancements in patient care. Full article

(This article belongs to the Special Issue Data Privacy and Security for Information Engineering)

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