Privacy Issues Research Articles

A unified component-based data-driven framework to support interoperability in the healthcare systems

Healthcare organizations must urgently prioritize interoperability to enhance the quality of care they provide. However, achieving this collaboration comes with numerous challenges, including differing approaches, data formats, and standards, as well as concerns about privacy, security, technical complexity, and legal and regulatory issues. To tackle these challenges, we determined a set of interoperability solutions. We also developed a comprehensive, component-based, data-driven framework for healthcare systems. Our study's approach involved three main steps: first, conducting a literature review to gather interoperability requirements and solutions from online databases and grey literature; second, carrying out a qualitative study to develop a framework based on the review results and focus group discussions; and third, using the Delphi method to validate the framework with experts. We extracted information from 36 articles during the screening and assessment process. Based on the proposed framework, we organized the identified themes into various categories, including architecture, architecture components, standards, platforms, policies, data sources, consumers, applications, level of interoperability, healthcare facilities, and considerations. Experts believe that establishing a comprehensive architecture for launching interoperability between health information systems can greatly facilitate this process. All framework components (totaling 197) received unanimous approval. The landscape of healthcare delivery is shifting from a focus on diseases to a patient-centered, data-driven approach. There is a growing demand for personalized healthcare systems, which necessitates increased interoperability among all healthcare stakeholders, particularly when dealing with diverse types of data. Our framework is designed to facilitate the implementation of various types of interoperability in healthcare systems.

DDP-FedFV: A Dual-Decoupling Personalized Federated Learning Framework for Finger Vein Recognition.

Finger vein recognition methods, as emerging biometric technologies, have attracted increasing attention in identity verification due to their high accuracy and live detection capabilities. However, as privacy protection awareness increases, traditional centralized finger vein recognition algorithms face privacy and security issues. Federated learning, a distributed training method that protects data privacy without sharing data across endpoints, is gradually being promoted and applied. Nevertheless, its performance is severely limited by heterogeneity among datasets. To address these issues, this paper proposes a dual-decoupling personalized federated learning framework for finger vein recognition (DDP-FedFV). The DDP-FedFV method combines generalization and personalization. In the first stage, the DDP-FedFV method implements a dual-decoupling mechanism involving model and feature decoupling to optimize feature representations and enhance the generalizability of the global model. In the second stage, the DDP-FedFV method implements a personalized weight aggregation method, federated personalization weight ratio reduction (FedPWRR), to optimize the parameter aggregation process based on data distribution information, thereby enhancing the personalization of the client models. To evaluate the performance of the DDP-FedFV method, theoretical analyses and experiments were conducted based on six public finger vein datasets. The experimental results indicate that the proposed algorithm outperforms centralized training models without increasing communication costs or privacy leakage risks.

Exploring ethical considerations in AI-driven cataloguing and classification with ChatGPT

Purpose. This study investigates the ethical dimensions surrounding the use of AI-driven cataloguing and classification systems, exemplified by ChatGPT, in library sciences. The research aims to fill a gap in the existing literature by exploring privacy, accountability, transparency, and bias issues associated with these technologies. Design/Methodology/Approach. The study employs a comprehensive literature review to analyze the current landscape of AI applications in libraries, emphasizing ethical considerations. It also assesses existing guidelines from associations like ALA and IFLA, and explores privacy concerns, biases, and the impact on job satisfaction. Findings. The study identifies gaps in current ethical frameworks and emphasizes the need for guidelines to evolve with technological advancements. Privacy concerns, biases in AI outputs, and challenges related to user trust and transparency are highlighted. The impact of AI on job satisfaction for librarians is discussed, acknowledging both opportunities and challenges. Originality/Value. This study contributes to the broader discussion on ethical AI by specifically addressing the underexplored area of AI-driven cataloguing and classification systems. It underscores the importance of aligning AI use with ethical standards, proposing strategies for mitigating biases and fostering user trust.

Penyalahgunaan Data Pribadi Sebagai Bentuk Kejahatan Sempurna Dalam Perspektlf Hukum Siber

The misuse of personal data has become a highly relevant issue in the digital age, where cases of privacy violations are increasing sharply. Personal data such as contact information, transaction history, and online activities have become prime targets for cybercriminals to use for fraud, identity theft, and even political manipulation. These crimes often occur in various locations around the world, taking advantage of regulatory breaks and jurisdictional differences, making law enforcement difficult. To address this problem, a multi-dimensional approach is required. Stricter regulations, such as the General Data Protection Regulation (GDPR) in the European Union, are needed to protect personal data with strict sanctions for violations. In addition, international collaboration is essential as cybercrime often crosses national borders, requiring close cooperation between countries to track and apprehend perpetrators. Public awareness and education are also key components in reducing the risk of cybercrime. People should be encouraged to take proactive measures such as the use of strong passwords and two-step verification. Companies and service providers also need to take responsibility for customer data security by implementing strong protection measures and transparency in data management. In conclusion, the use of personal data as a form of perfect crime is a complex issue, requiring strict regulation, international collaboration, and awareness raising to create a safer digital environment.

E-Rupee: Unlocking India's Digital Economy, Challenges, and Opportunities

This paper explores the concept of digital currency, differentiating between digitization and digitalization, and analyzes its different forms, specifically centralized and decentralized digital currencies. The analysis comprehensively examines Central Bank Digital Currencies (CBDCs), specifically focusing on India's e-rupee. It delves into the various forms of CBDCs, their management strategies, and the potential economic consequences they may have. The significance of digital currency in promoting financial inclusion, improving transaction efficiency, and influencing monetary policy is emphasized. Moreover, the study discusses the difficulties and dangers linked to the deployment of digital money, such as cyber-attacks, financial instability, privacy issues, and regulatory obstacles. The paper emphasizes the significance of strong risk management systems and consistent monitoring to guarantee the efficiency and safety of digital currencies. This study offers unique insights into the revolutionary potential of digital currencies and the strategic considerations required for their effective adoption by conducting a thorough assessment of the digital currency ecosystem. Moreover, the emergence of the 4th Industrial Revolution (4.0), driven by digital technologies, is transforming industries on a global scale within the field of agriculture, digitalization is transforming farming methods by implementing smart farming, precision agriculture, and digital agriculture. This paper also provides the guidelines for mitigating operational risks associated with digital currency, which may be found in the NIST Risk Management Framework and the CPMI-IOSCO Principles for Financial Market Infrastructures. Ultimately, the embrace of digital currencies and the digitization of numerous industries have the potential to stimulate economic growth, enhance financial accessibility, and proper technological advancement.

When Federated Learning Meets Privacy-Preserving Computation

Nowadays, with the development of artificial intelligence (AI), privacy issues attract wide attention from society and individuals. It is desirable to make the data available but invisible, i.e., to realize data analysis and calculation without disclosing the data to unauthorized entities. Federated learning (FL) has emerged as a promising privacy-preserving computation method for AI. However, new privacy issues have arisen in FL-based application because various inference attacks can still infer relevant information about the raw data from local models or gradients. This will directly lead to the privacy disclosure. Therefore, it is critical to resist these attacks to achieve complete privacy-preserving computation. In light of the overwhelming variety and a multitude of privacy-preserving computation protocols, we survey these protocols from a series of perspectives to supply better comprehension for researchers and scholars. Concretely, the classification of attacks is discussed including four kinds of inference attacks as well as malicious server and poisoning attack. Besides, this paper systematically captures the state of the art of privacy-preserving computation protocols by analyzing the design rationale, reproducing the experiment of classic schemes, and evaluating all discussed protocols in terms of efficiency and security properties. Finally, this survey identifies a number of interesting future directions.

SL: Stable Learning in Source-Free Domain Adaptation for Medical Image Segmentation

Deep learning techniques for medical image analysis often encounter domain shifts between source and target data. Most existing approaches focus on unsupervised domain adaptation (UDA). However, in practical applications, many source domain data are often inaccessible due to issues such as privacy concerns. For instance, data from different hospitals exhibit domain shifts due to equipment discrepancies, and data from both domains cannot be accessed simultaneously because of privacy issues. This challenge, known as source-free UDA, limits the effectiveness of previous UDA medical methods. Despite the introduction of various medical source-free unsupervised domain adaptation (MSFUDA) methods, they tend to suffer from an over-fitting problem described as “longer training, worse performance”. To address this issue, we proposed the Stable Learning (SL) strategy. SL is a method that can be integrated with other approaches and consists of weight consolidation and entropy increase. Weight consolidation helps retain domain-invariant knowledge, while entropy increase prevents over-learning. We validated our strategy through experiments on three MSFUDA methods and two public datasets. For the abdominal dataset, the application of the SL strategy enables the MSFUDA method to effectively address the domain shift issue. This results in an improvement in the Dice coefficient from 0.5167 to 0.7006 for the adaptation from CT to MRI, and from 0.6474 to 0.7188 for the adaptation from MRI to CT. The same improvement is observed with the cardiac dataset. Additionally, we conducted ablation studies on the two involved modules, and the results demonstrated the effectiveness of the SL strategy.

Smart Glasses in Surgery: The Theatre and Beyond.

The primary aim of this paper is to determine whether smart glasses or head-mounted displays improve efficiency in a procedural or theatre setting without compromising the quality of the procedure performed. Additionally, this paper aims to qualitatively explore applications in surgical education, whilst on-call, consulting and patient observation. This paper is a systematic review of the literature available on the topic of smart glasses or head-mounted displays in surgical or procedural settings. A search of Pubmed, Cochrane and the Wiley Online Library was performed in accordance with the PRISMA guidelines. Procedural times and adverse outcomes were compared between the smart glass and non-smart glass groups in each of the quantitative studies. A literature review of studies, including those not satisfying the primary aim was conducted and is included in this paper. 32 studies were identified that complied with the inclusion criteria of this paper. 8 of these studies focused on procedural times and adverse outcomes, with and without smart glass usage. Procedural time was reduced when smart glass technology was used, without an increase in adverse patient outcomes. Surgeons should consider whether the relatively short reduction in procedural time is worth the high cost, privacy issues, battery complaints and user discomfort involved with these devices. There are promising applications of this technology in the areas of surgical education and consultation. However, more trials are necessary to assess the value of using smart glasses in these settings.

Exploring Legal and Technical Challenges of Deep Fakes in India

The rapid advancement of deep fake technology has introduced significant challenges in the realms of data security, privacy, and intellectual property rights. Deep fakes, which involve the creation of highly realistic but manipulated digital content using artificial intelligence, have been misused for various malicious purposes, including political manipulation, identity theft, and defamation. This article examines the current state of deep fake regulation in India, highlighting the inadequacies of existing laws and the urgent need for specific legislation to address these issues. Despite the presence of broader laws under the Information Technology Act and the Indian Penal Code, there is a conspicuous absence of precise legal frameworks targeting the unique threats posed by deep fakes. Through an interdisciplinary approach that integrates legal analysis and technical insights, this study evaluates the effectiveness of current detection technologies and explores their limitations. The article also provides case studies illustrating the misuse of deep fakes in political and personal contexts, underscoring the real-world impact of this technology. In conclusion, the study advocates for comprehensive legal reforms and enhanced technological measures to mitigate the risks associated with deep fakes. By proposing targeted amendments to existing laws and recommending the development of advanced detection tools, this research aims to contribute to a more robust and effective regulatory environment for deep fakes in India.

Diabetic retinopathy identification based on multi-source-free domain adaptation.

To address the challenges of data labeling difficulties, data privacy, and necessary large amount of labeled data for deep learning methods in diabetic retinopathy (DR) identification, the aim of this study is to develop a source-free domain adaptation (SFDA) method for efficient and effective DR identification from unlabeled data. A multi-SFDA method was proposed for DR identification. This method integrates multiple source models, which are trained from the same source domain, to generate synthetic pseudo labels for the unlabeled target domain. Besides, a softmax-consistence minimization term is utilized to minimize the intra-class distances between the source and target domains and maximize the inter-class distances. Validation is performed using three color fundus photograph datasets (APTOS2019, DDR, and EyePACS). The proposed model was evaluated and provided promising results with respectively 0.8917 and 0.9795 F1-scores on referable and normal/abnormal DR identification tasks. It demonstrated effective DR identification through minimizing intra-class distances and maximizing inter-class distances between source and target domains. The multi-SFDA method provides an effective approach to overcome the challenges in DR identification. The method not only addresses difficulties in data labeling and privacy issues, but also reduces the need for large amounts of labeled data required by deep learning methods, making it a practical tool for early detection and preservation of vision in diabetic patients.

Research on Personal Medical Information Protection Based on Big Data

The rapid development of big data, cloud computing, and artificial intelligence has brought new opportunities to the development of the medical field. The emergence of electronic medical records and medical information systems has provided great convenience for people's medical treatment. However, the arrival of the big data era also poses new challenges to the protection of human medical privacy. The development and application of medical big data have led to explosive growth of data in the medical field, putting pressure on the management of medical institutions. The sharing of medical data between medical institutions and between medical institutions and third parties also poses significant security risks. Moreover, medical data leakage incidents have been continuously exposed in recent years, seriously infringing on the identity and privacy rights of patients. This article aims to explore the research on personal medical information protection based on big data. Firstly, the importance and necessity of protecting personal medical information were analyzed, as well as the current application status and existing problems of big data in medical information management. Secondly, a comparative analysis of domestic and international research on medical privacy protection was elaborated, including the analysis of medical information privacy protection standards in the United States, Europe, and Asia, and the differences and commonalities of international medical information privacy protection standards were compared and analyzed. Then, the theoretical basis of medical information privacy protection was summarized. Then, the application methods and technologies of big data in personal medical information protection were discussed. Finally, relevant countermeasures and suggestions for the protection of personal medical information in big data were discussed, including privacy protection technologies and policy recommendations, as well as ethical issues and norms for the protection of medical information privacy. Through this study, it is hoped that it can provide reference and inspiration for the protection of personal medical information in big data, and provide ideas and suggestions for future development directions.

New Media User Privacy Protection Mechanism Based on Differential Privacy and Data Anonymization

With the rapid development of new media, user privacy issues have become increasingly important. New media platforms, such as TikTok, Twitter, and WhatsApp, process vast amounts of user data daily, including personal information, behavioral data, and social relationships. The extensive collection and use of this data present numerous privacy protection challenges. Many users are not fully aware of how their data is collected and used when using new media platforms, lacking informed consent regarding data collection. Consequently, relying on users to take proactive privacy measures to prevent the disclosure of critical information is difficult to achieve. To address this issue, this paper proposes a privacy protection mechanism that combines differential privacy and data anonymization. The mechanism protects query results through differential privacy techniques and hides user identity information using data anonymization techniques, thereby ensuring user privacy during data analysis and processing. This paper conducts necessary experimental analysis on the proposed method, and the results demonstrate its usability and effectiveness.

A comparative Study on AI-Driven Anonymization Techniques for Protecting Personal Data

In the Artificial Intelligence era, protecting individual users' data has become crucial. The collected data is stored in multiple databases having personally identifiable information (PII). This may provide a significant privacy concern for the database. Several privacy-preserving approaches have been proposed, including Differential Privacy, Homomorphic Encryption, Generative Adversarial Network and Federated Learning. In this paper, the above four anonymization techniques are compared. In addition, this study will review the strengths and weaknesses of these techniques. We also discuss the trade-off between data utility and privacy. The results of this study aim to guide researchers and practitioners in selecting suitable AI-driven anonymization techniques.

Legal frameworks for digital transactions: Analyzing the impact of Blockchain technology

This study explores the legal frameworks governing digital transactions, with a specific focus on the transformative impact of blockchain technology. The primary aim is to elucidate the complexities and challenges posed by blockchain while examining the diverse regulatory approaches adopted internationally. Through a comprehensive literature review and comparative analysis, the research addresses key aspects such as the conceptual framework of digital transactions, the unique characteristics of blockchain, and the regulatory strategies implemented across different jurisdictions. The findings reveal that blockchain technology, characterized by its decentralized, immutable, and transparent nature, significantly disrupts traditional regulatory models. Identified challenges include jurisdictional ambiguities, enforcement difficulties, and privacy concerns. The comparative analysis shows divergent regulatory approaches: supportive frameworks in Japan and Switzerland contrast sharply with restrictive measures in China, highlighting the necessity for international cooperation and harmonization of regulations. The study concludes that effective regulation of blockchain technology requires innovative and flexible legal frameworks capable of adapting to rapid technological advancements. Policymakers must balance fostering innovation and protecting public interests, emphasizing the need for privacy-preserving technologies and international standards. Recommendations include developing global regulatory standards, enhancing privacy measures, and creating legal frameworks that accommodate the decentralized nature of blockchain systems. This research provides valuable insights for regulators, policymakers, and stakeholders, offering a pathway towards a secure, transparent, and innovative digital economy. Continuous adaptation and international collaboration are imperative to address emerging challenges and fully harness the potential of blockchain technology. The study advocates for proactive engagement and cooperation among nations to create a cohesive regulatory environment that promotes innovation while safeguarding public interests, enabling the global community to navigate the complexities of blockchain technology and unlock its full potential for economic and social advancement. Keywords: Blockchain Technology, Digital Transactions, Legal Frameworks, Regulatory Challenges, International Cooperation, Privacy Concerns.

Legal frameworks for digital transactions: Analyzing the impact of Blockchain technology

This study explores the legal frameworks governing digital transactions, with a specific focus on the transformative impact of blockchain technology. The primary aim is to elucidate the complexities and challenges posed by blockchain while examining the diverse regulatory approaches adopted internationally. Through a comprehensive literature review and comparative analysis, the research addresses key aspects such as the conceptual framework of digital transactions, the unique characteristics of blockchain, and the regulatory strategies implemented across different jurisdictions. The findings reveal that blockchain technology, characterized by its decentralized, immutable, and transparent nature, significantly disrupts traditional regulatory models. Identified challenges include jurisdictional ambiguities, enforcement difficulties, and privacy concerns. The comparative analysis shows divergent regulatory approaches: supportive frameworks in Japan and Switzerland contrast sharply with restrictive measures in China, highlighting the necessity for international cooperation and harmonization of regulations. The study concludes that effective regulation of blockchain technology requires innovative and flexible legal frameworks capable of adapting to rapid technological advancements. Policymakers must balance fostering innovation and protecting public interests, emphasizing the need for privacy-preserving technologies and international standards. Recommendations include developing global regulatory standards, enhancing privacy measures, and creating legal frameworks that accommodate the decentralized nature of blockchain systems. This research provides valuable insights for regulators, policymakers, and stakeholders, offering a pathway towards a secure, transparent, and innovative digital economy. Continuous adaptation and international collaboration are imperative to address emerging challenges and fully harness the potential of blockchain technology. The study advocates for proactive engagement and cooperation among nations to create a cohesive regulatory environment that promotes innovation while safeguarding public interests, enabling the global community to navigate the complexities of blockchain technology and unlock its full potential for economic and social advancement. Keywords: Blockchain Technology, Digital Transactions, Legal Frameworks, Regulatory Challenges, International Cooperation, Privacy Concerns.

Fall Detection Method for Infrared Videos Based on Spatial-Temporal Graph Convolutional Network.

The timely detection of falls and alerting medical aid is critical for health monitoring in elderly individuals living alone. This paper mainly focuses on issues such as poor adaptability, privacy infringement, and low recognition accuracy associated with traditional visual sensor-based fall detection. We propose an infrared video-based fall detection method utilizing spatial-temporal graph convolutional networks (ST-GCNs) to address these challenges. Our method used fine-tuned AlphaPose to extract 2D human skeleton sequences from infrared videos. Subsequently, the skeleton data was represented in Cartesian and polar coordinates and processed through a two-stream ST-GCN to recognize fall behaviors promptly. To enhance the network's recognition capability for fall actions, we improved the adjacency matrix of graph convolutional units and introduced multi-scale temporal graph convolution units. To facilitate practical deployment, we optimized time window and network depth of the ST-GCN, striking a balance between model accuracy and speed. The experimental results on a proprietary infrared human action recognition dataset demonstrated that our proposed algorithm accurately identifies fall behaviors with the highest accuracy of 96%. Moreover, our algorithm performed robustly, identifying falls in both near-infrared and thermal-infrared videos.

Reading Strategies in the AI Age: Enhancing Comprehension and Engagement with Advanced Technologies

The advent of artificial intelligence (AI) has transformed various facets of daily life, including how individuals approach reading and comprehension. This article explores the evolving landscape of reading strategies in the AI age, evaluating the impact of AI tools and applications on reading habits, comprehension techniques, and educational outcomes. With the rapid advancements in AI technologies, traditional reading methods are being supplemented, and in some cases, entirely replaced by AI-driven solutions. The discussion emphasizes AI-driven recommendations, which tailor reading materials to individual preferences and comprehension levels, thereby fostering more personalized and efficient learning experiences. Additionally, this article delves into the integration of AI in educational settings, where adaptive learning systems and intelligent tutoring platforms provide customized guidance and real-time feedback, significantly enhancing student engagement and achievement. However, the incorporation of AI in reading and education also presents potential challenges, including issues of data privacy, the digital divide, and the ethical implications of relying heavily on technology for cognitive development. By critically examining these multifaceted impacts, this article aims to provide a comprehensive understanding of how AI is reshaping reading strategies and to highlight the considerations necessary for harnessing its potential responsibly. Keywords: Artificial Intelligence, Reading Strategies, Comprehension, Personalized Learning, Educational Technology Proceedings Citation Format Ademola, E.O. (2024): Reading Strategies in the AI Age: Enhancing Comprehension and Engagement with Advanced Technologies. Proceedings of the 38th iSTEAMS Multidisciplinary Bespoke Conference. 15th–19th July, 2024. University of Ghana, Accra, Ghana. Pp 105-124. dx.doi.org/10.22624/AIMS/ACCRABESPOKE2024P13

Privacy implications of central bank digital currencies (CBDCs): a systematic review of literature

This study employs a systematic review of the literature to critically analyze the privacy implications of central bank digital currencies (CBDCs). Against the backdrop of a global digital revolution spurred by financial crises and the rise of cryptocurrencies, the emergence of CBDCs has garnered substantial attention from central banks worldwide. The study’s main objective is to fill a significant gap in the literature by providing an evidence-based assessment of the privacy issues of CBDCs. Through a meticulous analysis of the 46 selected papers, the study identifies key findings, including diverse perspectives on CBDC models, aspects and gaps in CBDC research, and various privacy concerns, such as extensive data collection in the direct CBDC model, challenges related to central bank data retention in the hybrid CBDC model, and increased operational complexity in the intermediated CBDC approach associated with CBDC implementation. The critical analysis points toward the urgent need for more extensive research into the privacy and security aspects of CBDCs to ensure user protection and trust. The findings contribute valuable insights for policymakers, regulators, and stakeholders in shaping the responsible creation and use of CBDCs.

Research on the Antecedents of Data Privacy Concern Toward Intelligent Connected Vehicles

Depending on Internet of Vehicle (IoV) and on-board units in 5 G, intelligent connected vehicles (ICVs) gradually become the digital infrastructure and data hub to provide personalized in-vehicle infotainment (IVI), which naturally collect, use, and share the data related to users’ bio-informatics, travel patterns, and usage habits. The privacy concern (PC) will be generated when using those data services. In this article, we construct the structural equation model (SEM) to study the influencing factors of users’ PC and self-disclosure behavior (SD) regarding to ICV data services from the personality traits (PTs) perspective. A total of 481 valid questionnaires from 700 IVI users was collected. Results show that extraversion (EXT), conscientiousness (CNS), neuroticism (NEUR), privacy invasion experience (PIE), and privacy self-efficacy (PE) have significant impacts on PC. Besides, PC does not directly affect SD, but is indirectly affected by limiting profile visibility (LPV). Through revealing the relationship among PTs, PC and SD toward ICV data services, we aim to provide insights for vehicle data governance to facilitate the human-vehicle interaction.

A Self-Sovereign Identity Privacy-Preserving Scheme for Logistics Transportation Based on One-Time-Use Tokens

The advancement of the logistics industry has fostered the enhancement of operational efficacy within the socioeconomic domain. However, the current inevitable privacy leaks in the process of logistics transportation have seriously affected the development of the industry, which led to a crisis of consumer trust and even caused economic recession. This paper proposes a self-sovereign identity privacy protection scheme tailored specifically for logistics transportation contexts. First, the scheme entails furnishing users with one-time-use tokens while establishing decentralized identities capable of concealing identity information and ensuring the secure transmission of data. Furthermore, the scheme integrates fuzzy identity-based encryption to encrypt identity information, thereby guaranteeing the confidentiality and integrity of logistics user identities along with their associated data. Compared with other schemes, this scheme exhibits superior security in the realm of logistics transportation. Its targeted encryption technology and self-sovereignty strategies address the critical issue of privacy leakage, thereby safeguarding consumer privacy rights and interests while facilitating the sustainable development of the logistics industry.