Privacy Challenges Research Articles

A critical literature review of security and privacy in smart home healthcare schemes adopting IoT & blockchain: Problems, challenges and solutions

Protecting private data in smart homes, a popular Internet-of-Things (IoT) application, remains a significant data security and privacy challenge due to the large-scale development and distributed nature of IoT networks. Recently, smart healthcare has leveraged smart home systems, thereby compounding security concerns in terms of the confidentiality of sensitive and private data and by extension the privacy of the data owner. However, proof-of-authority (PoA)-based blockchain distributed ledger technology (DLT) has emerged as a promising solution for protecting private data from indiscriminate use and thereby preserving the privacy of individuals residing in IoT-enabled smart homes. This review elicits some concerns, issues, and problems that have hindered the adoption of blockchain and IoT (BCoT) in some domains and suggests requisite solutions using the aging-in-place scenario. Implementation issues with BCoT were examined as well as the combined challenges BCoT can pose when utilised for security gains. The study discusses recent findings, opportunities, and barriers, and provides recommendations that could facilitate the continuous growth of blockchain applications in healthcare. Lastly, the study explored the potential of using a PoA-based permission blockchain with an applicable consent-based privacy model for decision-making in the information disclosure process, including the use of publisher-subscriber contracts for fine-grained access control to ensure secure data processing and sharing, as well as ethical trust in personal information disclosure, as a solution direction. The proposed authorisation framework could guarantee data ownership, conditional access management, scalable and tamper-proof data storage, and a more resilient system against threat models such as interception and insider attacks.

Exploring the General Data Protection Regulation (GDPR) compliance in cloud services: insights from Swedish public organizations on privacy compliance

The adoption of cloud services offers manifold advantages to public organizations; however, ensuring data privacy during data transfers has become increasingly complex since the inception of the General Data Protection Regulation (GDPR). This study investigates privacy concerns experienced by public organizations in Sweden, focusing on GDPR compliance. A qualitative interpretative approach was adopted, involving semi-structured interviews with seven employees from five public organizations in Sweden. Additionally, secondary data were gathered through an extensive literature review. The collected data were analyzed and classified using the seven privacy threat categories outlined in the LINDDUN framework. The key findings reveal several significant privacy issues when utilizing public cloud services, including unauthorized access, loss of confidentiality, lack of awareness, lack of trust, legal uncertainties, regulatory challenges, and loss of control. The study underscores the importance of implementing measures such as anonymization, pseudonymization, encryption, contractual agreements, and well-defined routines to ensure GDPR compliance. The findings emphasize the importance of implementing measures such as anonymization, pseudonymization, encryption, contractual agreements, and well-defined routines to ensure GDPR compliance. Furthermore, this research highlights the critical aspect of digital sovereignty in addressing privacy challenges associated with public cloud service adoption by public organizations in Sweden.

Thematic exploration of online privacy literacy and examination of its future agenda

ABSTRACT In today’s digitized society of ever-increasing information sharing, Online Privacy Literacy (OPL) is not only important to feel safe but be safe as well. OPL refers to skills and knowledge of individuals that helps to protect their privacy online. OPL is crucial in the digital era, where extensive data collection poses privacy challenges. The lack of OPL is a major disadvantage as it may impair one’s privacy decision-making abilities such as whether and how much personal data to disclose online. Despite its importance, OPL research is in its early stages. To pave the way to bridge this gap, we conducted a systematic review of 57 articles, offering a theoretical connection to privacy research. Our synthesis highlights how OPL is defined, measured, and its primary knowledge domains. Seven themes emerged, emphasizing OPL across age groups and revealing unexplored areas like the interplay between OPL, technology acceptance, and technostress. This exploration provides a roadmap for researchers to understand OPL implications and its role in privacy domains, guiding future research directions. Experts can leverage this thematic exploration to develop knowledge, skills, and effective privacy protection measures for individuals and institutions in the dynamic landscape of online privacy.

Health data security sharing method based on hybrid blockchain

With the rapid development of Internet of Things(IoT) technology, it is more and more common to use IoT devices to obtain health data. Health data sharing is a crucial component of health data utilization, yet it encounters serious privacy and security challenges. How to solve the problem of data security is the key factor to encourage users to participate, and existing data sharing methods generally have the problem of single point of failure. Based on this, this paper proposes a hybrid blockchain based health data sharing method HSHB, which divides sharing transactions into private chain data sharing and alliance chain data sharing according to different sharing entities. According to the identity and access purpose of different entities, HSHB has constructed the health data access control policy HDAC to avoid sharing interference among different entities. By building a b+ tree index of health data, HSHB provides users with an efficient and stable query scheme for historical health data. In addition, agent re-encryption and smart contract technology are used to share health data among institutions while ensuring the security of health data. Finally, the HSHB scheme is tested on XuperChain. The experimental results show that the health data sharing scheme HSHB proposed in this paper has advantages in computing overhead, throughput, and latency.

Multiomics tools for improved atherosclerotic cardiovascular disease management.

Multiomics studies offer accurate preventive and therapeutic strategies for atherosclerotic cardiovascular disease (ASCVD) beyond traditional risk factors. By using artificial intelligence (AI) and machine learning (ML) approaches, it is possible to integrate multiple 'omics and clinical data sets into tools that can be utilized for the development of personalized diagnostic and therapeutic approaches. However, currently multiple challenges in data quality, integration, and privacy still need to be addressed. In this opinion, we emphasize that joined efforts, exemplified by the AtheroNET COST Action, have a pivotal role in overcoming the challenges to advance multiomics approaches in ASCVD research, with the aim to foster more precise and effective patient care.

Using generative AI to turbocharge digital marketing

AI has been used in marketing for some time to enable better targeting and optimisation of messages, but some of the benefits of these approaches have been limited by the ability to create personalised content at scale and the ability to measure the effectiveness of this content in a structured way. Generative AI offers a way to address these challenges and to facilitate integrations that make it easier to execute and measure marketing in a fragmented ecosystem. However, the technology presents a series of technical, privacy and copyright challenges that organisations will need to overcome in order to use it effectively.

An efficient IoT group association and data sharing mechanism in edge computing paradigm

Despite its benefits and promising future, security and privacy challenges for the IoT wireless communication of edge computing environment remain unaddressed. As a result, proper authentication mechanisms are critical, especially in the extreme scenario where some edge facilities are not functional. For the above consideration, in this paper we develop an efficient IoT group association and updating mechanism in edge computing paradigm. The proposed scheme can provide data transmission and communication guarantees for special practical scenarios. The group key updating process in our architecture only necessitates minor changes on the EI side, whereas the decryption information of some IoT devices remains constant if the devices have not been revoked. The proposed strategy can accomplish the desired security features, according to the security analysis.

Empowering smart city governance through decentralized blockchain solutions for security and privacy in IoT communications

This paper highlights the benefits and challenges of smart cities, which leverage technology to offer a wide range of services to citizens. While these services have the potential to greatly improve the quality of life in metropolitan areas, they also raise significant privacy and security concerns. The study emphasizes the need to employ "privacy by design" principles to ensure that personal data is protected throughout the entire lifecycle of data, and data owners have the self-control to manage their data according to their preferences. Smart contracts, built on blockchain technology, offer a secure and transparent way of conducting transactions, particularly in e-governance, and automating processes. By leveraging these technologies, smart cities can address the privacy and security challenges they face while continuing to offer cutting-edge services to their inhabitants. Ultimately, the study emphasizes the importance of a proactive approach to privacy and security in the development and implementation of smart cities.

Alignment of Big Data Perceptions Across Levels in Healthcare: The case of New Zealand

Big data and related technologies have the potential to transform healthcare sectors by facilitating improvements to healthcare planning and delivery. Big data research highlights the importance of aligning big data implementations with business needs to achieve success. In one of the first studies to examine the influence of big data on business-IT alignment in the healthcare sector, this paper addresses the question: how do stakeholders’ perceptions of big data influence alignment between big data technologies and healthcare sector needs across macro, meso, and micro levels in the New Zealand (NZ) healthcare sector? A qualitative inquiry was conducted using semi-structured interviews to understand perceptions of big data across the NZ healthcare sector. An application of a novel theory, Theory of Sociotechnical Representations (TSR), is used to examine people’s perceptions of big data technologies and their applicability in their day-to-day work. These representations are analysed at each level and then across levels to evaluate the degree of alignment. A social dimension lens to alignment was used to explore mutual understanding of big data across the sector. The findings show alignment across the sector through the shared understanding of the importance of data quality, the increasing challenges of privacy and security, and the importance of utilising modern and new data in measuring health outcomes. Areas of misalignment include the differing definitions of big data, as well as perceptions around data ownership, data sharing, use of patient-generated data and interoperability. Both practical and theoretical contributions of the study are discussed.

Federated Learning; Privacy Preserving Machine Learning for Decentralized Data


 
 
 
 Federated learning represents a compelling solution for tackling the privacy challenges inherent in decentralized and distributed environments when it comes to machine learning. This scholarly paper delves deep into the realm of federated learning, encompassing its applications and the latest privacy-preserving techniques used for training machine learning models in a decentralized manner. We explore the reasons behind the adoption of federated learning, highlight its advantages over conventional centralized approaches, and examine the diverse methods employed to safeguard privacy within this framework. Furthermore, we scrutinize the current obstacles, unresolved research queries, and the prospective directions within this rapidly developing field
 
 
 

Advancing Through Data: A Critical Review of the Evolution of Medical Information Management Systems

The evolution of medical information management (MIM) is a critical aspect of modern healthcare, reflecting significant technological and regulatory advancements. This article offers a comprehensive review of the development and transformation of MIM systems, from rudimentary paper-based records to sophisticated digital platforms. We explore the early adoption of electronic health records (EHR), the integration of big data analytics, and the emerging role of artificial intelligence (AI) and machine learning in enhancing healthcare delivery. The review also addresses the pivotal challenges of data privacy and security, highlighting regulatory frameworks like HIPAA and GDPR. Through case studies, we analyze both successful implementations and notable challenges in the MIM field, providing a balanced perspective. The article discusses the crucial role of interoperability in facilitating efficient data exchange among diverse healthcare systems and underscores the importance of patient-generated data in future MIM systems. We conclude by identifying potential future trends, including the application of blockchain technology and the Internet of Things (IoT) in healthcare. This critical review serves as a guide for healthcare professionals, policymakers, and IT specialists, offering insights into the past, present, and future of medical information management.

Security, Privacy Challenges and Available Countermeasures in Electronic Health Record Systems: A Review

Electronic Health Record (EHR) systems have revolutionized the healthcare industry by enabling the efficient storage, retrieval, and sharing of patient health information. However, the widespread adoption of EHR systems has also engendered a myriad of privacy and security challenges that must be spoke to guarantee the privacy, integrity, and accessibility of sensitive patient evidence. A range of countermeasures has been implemented to reduce the risks associated with EHR data privacy and security. These include contact switch tools that limit data access to approved operators, as well as advanced encryption methods like Identity-Based Encryption and Attribute-Based Encryption, specifically tailored for securing EHR data in cloud environments. By employing these protective measures, healthcare providers can enhance the safety of sensitive patient information while adapting to the dynamic digital landscape.

The Digital Transformation of the Banking Industry in the Era of Big Data

Big data analytics has become an instrumental tool for digital transformation in the banking industry. By leveraging massive volumes of data, banks can gain valuable insights to optimize operations, reduce risks, and improve customer experience. For risk management, big data techniques enable banks to detect fraud and money laundering activities more accurately. In customer relationship management, big data analytics allows banks to better understand customer needs, provide personalized services, and enhance loyalty. Furthermore, big data can inform investment decisions by identifying new opportunities and mitigating risks. While big data brings significant opportunities, it also presents challenges in data governance, privacy, and security that require prudent management. Technical and business model innovations must align to fully realize the potential of big data. Banks should invest in big data infrastructure, promote a data-driven culture, and pursue strategic partnerships. Regulators and policymakers should establish flexible guidelines to encourage big data adoption while protecting consumer privacy. By harnessing big data responsibly, banks can transform into truly customer-centric digital organizations. Overall, big data is redefining the banking industry, and continuous digital transformation is imperative for banks to thrive in the new data-driven era.

Strengthening Privacy and Data Security in Biomedical Microelectromechanical Systems by IoT Communication Security and Protection in Smart Healthcare

Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection.

Big data-analysis, map reduced framework, security & privacy challenges and techniques in health sector

INTRODUCTION: Data is increasing exponentially. Data processing is an essential component in all industries, including health care. Even though a lot of progress has been made, it has been noted that in the recent decade, the health industry is capable of efficiently utilizing data and providing perfect Advancements in therapies.
 OBJECTIVES: the main objectives include of finding the right problems in the security systems and to review the methods of present data processing methods.
 METHODS: Methods involved are Quantitive analysis, Descriptive analysis, Data cleaning and Extraction.
 RESULTS: The outputs of the reduce function are combined across all reducer nodes to produce the final output.
 CONCLUSION: Big data analytics has enormous potential to accelerate the health care industry and that can only be done with some innovative methods and security plays a crucial role and can be a good catalyst in the user experience elements.

ChatGPT in orthopedics: a narrative review exploring the potential of artificial intelligence in orthopedic practice.

The field of orthopedics faces complex challenges requiring quick and intricate decisions, with patient education and compliance playing crucial roles in treatment outcomes. Technological advancements in artificial intelligence (AI) can potentially enhance orthopedic care. ChatGPT, a natural language processing technology developed by OpenAI, has shown promise in various sectors, including healthcare. ChatGPT can facilitate patient information exchange in orthopedics, provide clinical decision support, and improve patient communication and education. It can assist in differential diagnosis, suggest appropriate imaging modalities, and optimize treatment plans based on evidence-based guidelines. However, ChatGPT has limitations, such as insufficient expertise in specialized domains and a lack of contextual understanding. The application of ChatGPT in orthopedics is still evolving, with studies exploring its potential in clinical decision-making, patient education, workflow optimization, and scientific literature. The results indicate both the benefits and limitations of ChatGPT, emphasizing the need for caution, ethical considerations, and human oversight. Addressing training data quality, biases, data privacy, and accountability challenges is crucial for responsible implementation. While ChatGPT has the potential to transform orthopedic healthcare, further research and development are necessary to ensure its reliability, accuracy, and ethical use in patient care.

The Proposed Framework for Cloud Computing System Security and Privacy Services

Public cloud computing has emerged as a popular and efficient solution for organizations to store, process, and access their data and applications. However, with the increasing adoption of public cloud services, concerns regarding security have become a critical factor in decision-making processes. This study aims to investigate the security aspects of public cloud computing and identify potential vulnerabilities and threats that organizations may face. The aim of this study is to examine the challenges associated with cloud computing and its security concerns. Cloud computing is a technology that utilizes the internet to share data and resources. It is characterized by five key features: On-Demand self-service, Resource Pooling, Broad network access, Rapid elasticity, and Measured Service. However, despite these benefits, cloud computing has several open issues, including Security, Availability, Scalability, and Interoperability. Traditional security techniques are insufficient in fully securing cloud computing. Security and privacy issues often arise during cloud deployment and delivery models. As a result, researchers are interested in learning more about cloud computing security and its methods. Industries are creating architectural models with a high level of protection for their data center to solve current security challenges. This article examines the security of cloud deployment strategies and analyzes cloud security and privacy challenges. The cloud computing service models are also discussed, along with any security issues. The study emphasizes the significance of Audit as a key factor in offering a security solution that covers all relevant security characteristics as determined by the literature. The study's conclusions present a fresh challenge to cloud service providers and suggest a new security paradigm that explains customer security needs. Overall, this study offers a parameter-by-parameter assessment of available security solutions and makes recommendations for enhancements to address cloud security issues.

Exploring Machine Learning Intrusion Detection: Addressing Security and Privacy Challenges in IoT - A Comprehensive Review

With billions of IoT devices in operation globally, vast amounts of data are generated, posing significant security challenges throughout the data lifecycle. Machine learning (ML) offers a promising approach to safeguarding IoT systems by swiftly detecting anomalies and enforcing real-time security and privacy (S&P) measures. This systematic literature review investigates ML-based intrusion detection in IoT, examining academic journals from 2011 to 2021 through the IEEE and ProQuest databases. Utilizing the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework, we identify key insights and challenges. Our review reveals that while ML-based Intrusion Detection Systems (IDS) exhibit superior performance in detecting emerging attack trends, they also introduce complexities such as increased computational demands, susceptibility to adversarial attacks, scalability issues, and trade-offs between accuracy and false positives. Furthermore, deep learning methods outperform traditional ML techniques in anomaly detection. Addressing the evolving nature of attacks remains a continuous endeavor, underscoring the ongoing development of IDS.

Architectural Design of a Blockchain-Enabled, Federated Learning Platform for Algorithmic Fairness in Predictive Health Care: Design Science Study.

Developing effective and generalizable predictive models is critical for disease prediction and clinical decision-making, often requiring diverse samples to mitigate population bias and address algorithmic fairness. However, a major challenge is to retrieve learning models across multiple institutions without bringing in local biases and inequity, while preserving individual patients' privacy at each site. This study aims to understand the issues of bias and fairness in the machine learning process used in the predictive health care domain. We proposed a software architecture that integrates federated learning and blockchain to improve fairness, while maintaining acceptable prediction accuracy and minimizing overhead costs. We improved existing federated learning platforms by integrating blockchain through an iterative design approach. We used the design science research method, which involves 2 design cycles (federated learning for bias mitigation and decentralized architecture). The design involves a bias-mitigation process within the blockchain-empowered federated learning framework based on a novel architecture. Under this architecture, multiple medical institutions can jointly train predictive models using their privacy-protected data effectively and efficiently and ultimately achieve fairness in decision-making in the health care domain. We designed and implemented our solution using the Aplos smart contract, microservices, Rahasak blockchain, and Apache Cassandra-based distributed storage. By conducting 20,000 local model training iterations and 1000 federated model training iterations across 5 simulated medical centers as peers in the Rahasak blockchain network, we demonstrated how our solution with an improved fairness mechanism can enhance the accuracy of predictive diagnosis. Our study identified the technical challenges of prediction biases faced by existing predictive models in the health care domain. To overcome these challenges, we presented an innovative design solution using federated learning and blockchain, along with the adoption of a unique distributed architecture for a fairness-aware system. We have illustrated how this design can address privacy, security, prediction accuracy, and scalability challenges, ultimately improving fairness and equity in the predictive health care domain.

The state of and prospects for the application of blockchain technology in e-commerce

Whilst e-commerce continues to grow, it has met privacy, trust, and security challenges. Blockchain technology is among the solutions that e-commerce can use to address these challenges. Blockchain technology poses benefits but comes with its challenges. The purpose of this study was to systematically review literature that focused on the state and prospects of blockchain technology in e-commerce. Specifically, the research question is what is the state of the application of blockchain technology in e-commerce? The study conducted searches using four databases (EbscoHost, Science Direct, IEEE Explore, and Jstor) as well as Google Scholar. Exclusion and inclusion criteria were applied in the searches to answer the research question. In total, 25 articles were used for this research and were thematically analysed. The results showed that while there are many benefits in implementing blockchain technologies in e-commerce, there are also challenges. The benefits include an increase in privacy, enhanced security, and minimized costs of transactions. The challenges are tax evasion, black market transactions, and lack of regulation. Results also showed blockchains are used to track product movement and originality. The study's novel contribution is to theory by developing a model for the application, benefits, and challenges of the blockchain technology ecosystem in e-commerce. The study contributes to policy by focusing on sustainable development goals 7 and 9. The research also contributes to the body of knowledge on the application of blockchain. Businesses, scholars, government officials, and other stakeholders can use this study to understand the application, challenges, and benefits of blockchain in e-commerce.