Privacy Issues Research Articles

Mobile-linked point-of-care diagnostics in community-based healthcare: a scoping review of user experiences

BackgroundWhile mobile-linked point-of-care diagnostics may circumvent geographical and temporal barriers to efficient communication, the use of such technology in community settings will depend on user experience. We conducted a scoping review to systematically map evidence on user experiences of mobile-linked point-of-care diagnostics in community healthcare settings published from the year 2016 up to the year 2022.MethodologyWe conducted a comprehensive search of the following electronic databases: Scopus, Web of Science, and EBSCOhost (Medline, CINAHL, Africa-wide, Academic Search Complete). The inter-reviewer agreement was determined using Cohen’s kappa statistic. Data quality was appraised using the mixed method appraisal tool version 2018, and the results were reported according to the preferred reporting items for systematic reviews and meta-analyses for scoping reviews (PRISMA-ScR).ResultsFollowing the abstract and full article screening, nine articles were found eligible for inclusion in data extraction. Following the quality appraisal, one study scored 72.5%, one study scored 95%, and the remaining seven studies scored 100%. Inter-rater agreement was 83.54% (Kappa statistic = 0.51, p < 0.05). Three themes emerged from the articles: approaches to implementing mobile-linked point-of-care diagnostics, user engagement in community-based healthcare settings, and limited user experiences in mobile-linked point-of-care diagnostics. User experiences are key to the sustainable implementation of mobile-linked point-of-care diagnostics. User experiences have been evaluated in small community healthcare settings. There is limited evidence of research aimed at evaluating the usability of mobile-linked diagnostics at the community level.ConclusionMore studies are needed to assess the user experience of mobile-linked diagnostics in larger communities. This scoping review revealed gaps that need to be addressed to improve user experiences of mobile-linked diagnostics, including language barriers, privacy issues, and clear instructions.

Unveiling Users' Security and Privacy Concerns Regarding Smart Home IoT Products from Online Reviews

The Internet of Things (IoT) has revolutionized the global market with lifestyle products such as fitness trackers (FT), smart home speakers (SHS), and surveillance and security camera systems (SSCS). While offering convenience, these products also introduce potential security and privacy (S&amp;P) risks to buyers, often going unnoticed. Consumers’ incomplete mental models of the risks involved and the information asymmetry between buyers and sellers only add to the problem. Understanding consumer concerns in online product reviews can play a crucial role in bridging the gap of such information asymmetry. By establishing a balanced flow of information between buyers and sellers, manufacturers can leverage genuine concerns expressed in reviews to enhance product features while educating users about misinformation in reviews. In this study, we collected FT, SHS, and SSCS product reviews from three Amazon domains: the US, the UK, and India. Using a keyword-based search method focused on S&amp;P concerns, we discovered a considerable number of reviews expressing notable concerns regarding security and privacy. Our qualitative analysis revealed that data security is a common concern across all product types. Further, our quantitative analysis exposed significant geographic variations, with the concern ratio being higher in the US than in the UK for all device types and higher than in the Indian domain for security cameras. These findings highlight the need for tailored security measures and user awareness campaigns in different parts of the world to address the identified concerns effectively.

Unveiling the social media landscape: exploring factors influencing usage patterns among tertiary education students in Ghana

AbstractThis research assessed the factors affecting social media usage by tertiary education students in Ghana. The technology acceptance model underpinned the study. A total of 513 tertiary education students selected from across the country completed a set of questionnaires using Google Forms. Frequencies, percentages, means, independent samples t-test, and ANOVA were used as the analytical framework for the study. Although respondents were very highly aware of social media platforms such as Facebook, Instagram, and WhatsApp, they were less aware of platforms such as Flicker, SoundCloud, Evernote, Pinterest, 2go, and MySpace. In addition, respondents reported that the extent to which social media was used for information sharing, entertainment, education, interpersonal connectivity, and social bonding was high. The level at which social media was used for self-promotion, impression management, self-disclosure, and self-documentation, and displaying creativity was moderate. The major factors respondents considered when using social media platforms were the convenience of the platforms, their experience with the platforms, their perceived usefulness, social influence, and the cost of the platforms. Despite their importance, legal risks and privacy issues did not strongly feature in the factors respondents considered before using social media platforms. Significant differences existed in the respondents’ views on the factors affecting their social media platform usage based on age. Tertiary education students in Ghana are advised to maximise their use of social media platforms for educational purposes. Finally, to avoid inconveniences, Ghanaian tertiary education students should pay increased attention to the legal implications and privacy issues associated with social media adoption.

Legal Protection of Children's Personal Data in the Digital Era

In the rapidly evolving digital era, the use of digital technology has become an integral part of everyday life, including for children. However, this development also raises concerns about the protection of children's personal data in the digital environment, which is vulnerable to misuse and exploitation. The purpose of this study is to determine and analyze the legal protection of children's personal data in Indonesia in the context of the digital era and explore the negative impact of the abuse of children's privacy rights in the digital environment, using a normative juridical research method with a statutory approach. The results show that in Indonesia there are several laws that protect children's personal data, such as the Child Protection Act, the Electronic Information and Transaction Act, the Personal Data Protection Act, the Minister of Communication and Information Technology Regulation, and the Government Regulation on Compensation for Children Who Are Victims of Criminal Acts. The negative impacts of the misuse of children's privacy include the risk of cybercrime, impaired child development, and emotional trauma. Therefore, efforts to improve the protection of children's personal data in the digital era are still needed by the government, technology platform providers, and parents to create a safe online environment that supports children's growth and development. The findings of this study have significant implications for policymakers, legal practitioners, and stakeholders involved in child protection in the digital space. Strengthening the legal framework and ensuring its effective implementation are important steps to protect children's personal data from misuse.

Analysis of user acceptance of information and communications technology for electrical safety inspection based on a choice experiment and hierarchical Bayesian model

The development of information and communications technology has resulted in changes in electrical safety inspection. Electrical safety inspectors plan to introduce smart devices that detect causes of extensive electrical fires in real time without problems of power disconnections and on-site visits. However, smart inspection is expected to encounter acceptance issues because users are unaccustomed to it and obligated to pay additional costs for device installation and data communications. This study aims to investigate smart inspection acceptance using survey data. A hierarchical Bayesian model is employed to explore the effects of users' characteristics on its acceptance. The respondents prefer attributes of smart inspection to those of the prevailing method of on-site inspection, excluding monthly inspection costs. They prefer more frequent and extensive inspections, and prefer to avoid power disconnections and physical interaction. Accordingly, the government should inform users that smart inspection is convenient and accurate. It is also attractive to users who wish to avoid on-site visits because of privacy issues. The acceptance rate can increase if the government reduces inspection costs using the existing smart devices and communications infrastructure, and offers real-time electrical safety information using smart phones and in-home displays.

E-Tenon: An efficient privacy-preserving secure open data sharing scheme for EHR system

The transition from paper-based information to Electronic-Health-Records (EHRs) has driven various advancements in the modern healthcare industry. In many cases, patients need to share their EHR with healthcare professionals. Given the sensitive and security-critical nature of EHRs, it is essential to consider the security and privacy issues of storing and sharing EHR. However, existing security solutions excessively encrypt the whole database, thus requiring the entire database to be decrypted for each access request, which is time-consuming. On the other hand, the use of EHR for medical research (e.g., development of precision medicine and diagnostics techniques) and optimisation of practices in healthcare organisations require the EHR to be analysed. To achieve that, they should be easily accessible without compromising the patient’s privacy. In this paper, we propose an efficient technique called E-Tenon that not only securely keeps all EHR publicly accessible but also provides the desired security features. To the best of our knowledge, this is the first work in which an Open Database is used for protecting EHR. The proposed E-Tenon empowers patients to securely share their EHR under their own multi-level, fine-grained access policies. Analyses show that our system outperforms existing solutions in terms of computational complexity.

Exploring the Landscape: The Societal Impact of Social Media Marketing

In the digital age, social media marketing has drastically altered social connections and customer behavior. With an emphasis on how social media marketing influences consumer attitudes, cultural norms, and mental health, this study explores the numerous ways that social media marketing impacts society. As businesses utilize Facebook, Instagram, and Twitter more and more to communicate with big audiences, understanding these consequences is critical. The study aims to determine how social media marketing affects, among other aspects of society, social interactions, cultural perspectives, consumer behavior, and mental health. The study makes use of secondary data collection methods, including a comprehensive survey of the literature. Thematic analysis is used to find patterns and gather data. Significant research demonstrates that social media marketing significantly increases consumer involvement through customized campaigns and user-generated content, which fortifies brand loyalty and influences consumer behavior. It does, however, also reinforce cultural norms and prejudices while offering the possibility of societal transformation. Virtual communities are known to be altering social interactions, but using them excessively might lead to social isolation. Negative pressure to maintain idealized online personas and constructive criticism are both hazardous to mental health. The growing importance of influencer marketing is emphasized, but so are privacy and ethical issues around data collection and targeted advertising. With cutting-edge technologies like augmented reality, virtual reality, and artificial intelligence, social media marketing is set to undergo yet another revolution. The study offers several recommendations, such as enhancing the personalization of marketing campaigns, utilizing user-generated content, balancing cultural representation, lessening the detrimental effects of social interactions, prioritizing mental health, addressing ethical and privacy issues, ensuring that influencer marketing is genuine, and embracing emerging technologies. Future research on these processes is warranted, focusing on the moral implications and challenges of developing technologies. This in-depth investigation highlights how important it is to act ethically and continuously adapt to the ever-changing world of social media marketing.

Unveiling the Power of the Internet of Things: Exploring Services, Applications, and Overcoming Challenges

The Internet of Things (IoT) has transcended its futuristic perception and become an omnipresent reality. Its pervasive nature encompasses devices, sensors, clouds, big data, and business interactions. This revolutionary concept amalgamates traditional embedded systems with wireless microsensors, automation-driven control systems, and other elements to establish a vast infrastructure. The integration of wireless communication, micro electro mechanical devices, and the Internet has given rise to novel IoT applications. The IoT is essentially a network of interconnected objects accessible through the Internet, each object uniquely identifiable. The advent of IPv6, superseding IPv4, plays a pivotal role in expanding the address space for IoT development. The primary objective of IoT applications is to imbue objects with intelligence, eliminating the need for human intervention. However, the proliferation of smart nodes and the exponential data generated by each node present new challenges pertaining to data privacy, scalability, security, manageability, and other critical issues, which we delve into in this comprehensive exploration

Deep Feed-Forward Neural Network-Based Biometric Authentication System with Biometric Identity and Reputation Score in Blockchain

The biometric authentication in today’s digital era has become a vital issue in security and privacy. Biometrics is currently becoming more significant specifically for higher security modules. Here, a deep feed-forward neural network-based biometric authentication system (DFFNN_biometric authentication system) is presented for biometric authentication utilizing a biometric fingerprint image. In blockchain network, biometric data are considered and the fingerprint images are fed as input. As an input fingerprint image, minutiae extraction is accomplished. Thereafter, a deep key is generated employing a deep residual network (DRN) for template protection. Thereafter, privacy protection template generation is conducted and the templates thus obtained are stored on a database (DB). On the other hand, a query fingerprint image is given as input in the authentication stage. The processes such as minutiae extraction, deep key generation using DRN and privacy protection template authentication are carried out as like it performed in the blockchain network. Additionally, the matching process is conducted between the output acquired in the authentication phase and image stored in DB. The reputation score is generated from authenticated biometric behavior. The DFFNN is utilized to generate a reputation score. Moreover, the DFFNN_biometric authentication system obtained a maximum accuracy of 90.8%, maximum genuine accept rate (GAR) of 0.936, minimum false acceptance rate (FAR) of 0.588, minimum false rejection rate (FRR) of 0.556 and maximum reputation score of 9.346.

Risks and Legal Governance of Generative Artificial Intelligence

Generative Artificial Intelligence (AI) represents a significant advancement in the field of artificial intelligence, characterized by its ability to autonomously generate original content by learning from existing data. Unlike traditional decision-based AI, which primarily aids in decision-making by analyzing data, generative AI can create new texts, images, music, and more, showcasing its immense potential across various domains. However, this technology also presents substantial risks, including data security threats, privacy violations, algorithmic biases, and the dissemination of false information. Addressing these challenges requires a multi-faceted approach involving technical measures, ethical considerations, and robust legal frameworks. This paper explores the evolution and capabilities of generative AI, outlines the associated risks, and discusses the regulatory and legal mechanisms needed to mitigate these risks. By emphasizing transparency, accountability, and ethical responsibility, we aim to ensure that generative AI contributes positively to society while safeguarding against its potential harms.

The Evolution of Privacy Rights in the Digital Age: A Comparative Analysis of GDPR and CCPA

The Evolution of Privacy Rights in the Digital Age: A Comparative Analysis of GDPR and CCPA

Enhancing fraud detection efficiency in mobile transactions through the integration of bidirectional 3d Quasi-Recurrent Neural network and blockchain technologies

Cases of financial fraud are increasing despite recent technical breakthroughs. It is tough to find authentic financial transaction data due to privacy issues and a lack of inter-organization synergy. However, for technologies based on data, such as machine learning to function accurately inside practical systems, they require actual data. This manuscript proposes a Fraud Detection (FD) Efficiency in Mobile Transactions through the Integration of Bidirectional 3D Quasi-Recurrent Neural Networks and Proof of Voting consensus Blockchain Technologies (Bi-3DQRNN-PoV-FD-MT). A dataset of Bitcoin transactions is employed in the suggested model. The banking sector’s Bitcoin transactions serve as the foundation for this dataset. Then the data balancing is performed using a Self-Adaptive Synthetic Over-Sampling Technique (SASOS). Then, the proposed framework utilizes the Bi-3DQRNN to determine if the information is fraudulent or not. Moreover, an intelligent Enhanced Artificial Gorilla Troops (EAGTO) Optimization algorithm is introduced to tune the weights of the model parameters. A blockchain-based proof of Voting (PoV) consensus algorithm is integrated with the proposed model for forecasting upcoming transactions. The developed scheme is instigated in Python and the performance provides 12.09%, 8.91%, and 6.92% higher accuracy compared with existing techniques like K-nearest neighbor- Distributed Blockchain Consortium (KNN-DBC), Decision tree-Ethereum blockchain-enabled smart contract (DT-EBSC) and Heterogeneous Graph Transformer Networks-Ethereum smart contract (HGTN-ESC).

Empowering IoT Developers with Privacy-Preserving End-User Development Tools

Internet of Things applications (IoT) have the potential to derive sensitive user data, necessitating adherence to privacy and data protection laws. However, developers often struggle with privacy issues, resulting in personal data misuse. Despite the proposed Privacy by Design (PbD) approach, criticism arises due to its ambiguity and lack of practical tools for educating software engineers. We introduce Canella, an integrated IoT development ecosystem with privacy-preserving components leveraging End-User Development (EUD) tools Blockly@rduino and Node-RED, to help developers build end-to-end IoT applications that prioritize privacy and comply with regulations. It helps developers integrate privacy during the development process and rapid prototyping phases, offering real-time feedback on privacy concerns. We start by conducting a focus group study to explore the applicability of designing and implementing PbD schemes within different development environments. Based on this, we implemented a proof-of-concept prototype of Canella and evaluated it in controlled lab studies with 18 software developers. The findings reveal that developers using Canella created more privacy-preserving applications, gained a deeper understanding of personal data management, and achieved better privacy compliance. Our results also highlight Canella's role in educating and promoting privacy awareness, enhancing productivity, streamlining privacy implementation, and significantly reducing cognitive load. Overall, developers found Canella and its privacy-preserving components useful, easy to use, and easy to learn, which could potentially improve IoT application privacy. Watch the demo video.

Using Natural Language Processing on Expert Panel Discussions to Gain Insights for Recruitment, Retention and Intervention Adherence for Online Social Support Interventions on a Stage II-III Clinical Trial Among Hispanic and African American Dementia Caregivers.

We applied natural language processing (NLP) to a corpus extracted from 4 hours of expert panel discussion transcripts to determine the sustainability of a Stage II-III clinical trial of online social support interventions for Hispanic and African American dementia caregivers. Prominent topics included Technology/hard to reach populations, Training younger populations, Building trust, Privacy and security issues, Simplification of screening questions and recruitment procedures, Understanding participants' needs, Planning strategies and logistics, Potential recruitment places, Adjusting intervention size downwards to engage elderly participants, Targeting different generations, Internet-based interventions by age range, and Providing step-by-step instructions and an overview of the entire research process during recruitment. The application of NLP to qualitative data on a dementia caregiving clinical trial provides useful insights for recruitment, retention, and adherence to guidelines for such interventions serving Hispanic and African American dementia caregivers.

Synthetic Generation of Patient Service Utilization Data: A Scalability Study.

To address privacy and ethical issues in using health data for machine learning, we evaluate the scalability of advanced synthetic data generation methods like GANs, VAEs, copulaGAN, and transformer models specifically for patient service utilization data. Our study examines five models on data from a Canadian health authority, focusing on training and generation efficiency, data resemblance, and practical utility. Our findings indicate that statistical models excel in efficiency, while most models produce synthetic data that closely mirrors real data, and is also useful for real-world applications.

GenAI in the Context of African Universities: A Crisis of Tertiary Education or Its New Dawn?

The rapid progression of generative artificial intelligence (GenAI) tools has raised significant interest and concern in academia. Instances of students submitting AI-generated assignments prompt investigations into implications for teaching, learning, and academic integrity. Recent publications highlight concerns such as a lack of conceptual understanding, threats to academic integrity, and disruptions to traditional assessment methods. While recognizing benefits like automated scoring and personalized learning, authors stress the responsible use of GenAI, emphasizing the educator's role in guiding students. This commentary identifies opportunities and threats of GenAI in African university contexts. Opportunities include increased operational efficiency, content generation, automated assessment, recognition of accessibility needs, overcoming language barriers, and accelerated research. However, these tools require human correction and cautious consideration of job displacement concerns. Threats encompass job displacement, privacy and security issues, threats to academic integrity, hallucinations/confabulations of GenAI, access and infrastructure challenges, technological overemphasis, lack of customization for local needs and cultural contexts, dependency on external providers, and unaffordable costs. The need for robust guidelines that balance technological advances with traditional teaching methods in African universities is emphasized. Given digital transformation initiatives like the African Union's Agenda 2063 and Botswana's SmartBots strategy, integrating GenAI could shape the future of African tertiary education. Proactive policies should address ethical concerns, ensure access, and make GenAI tools available, requiring a collaborative effort to navigate its impact responsibly.

Five years with the GDPR: an empirical study emphasising information privacy and the consumer

Consumers’ privacy rights have been enshrined in law, long before information systems and the Internet was brought to life. In 2018, stricter regulations relating to information privacy came into force, named the General Data Protection Regulation (GDPR). Using elements of Roger’s diffusion of innovations theory, we investigated the research question: How has five years of the GDPR influenced consumer’s knowledge, attitude, and practice of their enhanced rights? We draw on empirical data collected in Norway through four online survey questionnaires over five years (N=1293). Quantitative (descriptive statistics) and qualitative analyses (manual cluster text mining) were performed to obtain a state-of-the-art mapping of insights on consumers and their information privacy. Our findings show that the respondents’ answers remained similar over the years, and that the GDPR has not had a significant influence on the consumer. The respondents demonstrated a high degree of knowledge regarding both the regulation and technology, such as cookies. Their attitude was sceptical, as they valued their enhanced rights but questioned the feasibility. Regarding their practice, our findings reveal diversity. Some respondents took careful actions to protect their privacy, while most did not. The present paper should be interesting to both the industry (practitioners) and academia (researchers).

The role of artificial intelligence in preventing corporate crime

This article explores the novel and transformative role AI plays in enhancing corporate compliance and monitoring to mitigate corporate crime. It delves into how AI technologies, through advanced data analysis and predictive analytics, are revolutionizing the detection and prevention of financial fraud, insider trading, and non-compliance with environmental regulations. The integration of AI into the financial sector is emphasized as pivotal for developing robust fraud prevention algorithms, which analyze vast data volumes to identify malicious activities and prevent financial losses. Moreover, the article probes into the ethical and privacy concerns surrounding the deployment of AI, highlighting the balance needed between efficient crime prevention and safeguarding individual privacy rights. Garcia-Segura's analysis also underscores the need for international cooperation and legal adaptation to uphold ethical standards in AI’s application, ensuring it promotes fairness and justice in combating corporate crime.

Comprehensively enhancing the security of control with combined homomorphic encryption

Homomorphic encryption is an effective way to address the privacy and security issues of Networked Control Systems (NCSs). Since the control function needs to be redesigned according to the homomorphism to complete encrypted computing, the practical implementation of a perfectly secure and highly efficient NCS is challenging. Previously proposed NCSs based on homomorphic encryption are still subject to the risk of eavesdropping attacks. In this paper, a combined homomorphic encryption scheme is designed to build a secure environment for NCSs. This scheme comprehensively enhances the security of NCSs by eliminating potential security hazards. The risk of eavesdropping attacks on information in the controller and communication channel is avoided. More specifically, the entire control scheme is encrypted and privacy computing within the controller is performed on this basis. Data protection is provided for all transmission channels, including the transmission of the intermediate result and controller state. In particular, the computational efficiency of the encrypted control system is fast and feasible for real-time control. The performance and stability of the closed-loop system are maintained.

StarCross: Redactable blockchain-based secure and lightweight data sharing framework for satellite-based IoT

Due to limitations in network coverage and capacity, terrestrial networks cannot meet the growing demand for widespread intelligent applications in recent years. The increasingly popular Satellite-based Internet of Things (S-IoT), with its extensive and boundless coverage, has emerged as the optimal solution. However, the large-scale distributed communication and services have led to significant challenges in scalability and security. Blockchain can help mitigate security and privacy issues in data sharing of S-IoT. To address these challenges, we propose StarCross, a secure and lightweight data sharing framework for S-IoT based on redactable blockchain. Firstly, we develop a space-ground collaborative sharding blockchain network architecture. Low Earth Orbit (LEO) satellite network serves as the communication intermediary between shards. Secondly, StarCross employs an improved Proof of Work (PoW) consensus based on dynamic difficulty to mitigate the challenges of uneven computing power and centralization within each shard. To ensure the atomicity of cross-shard transactions, StarCross introduces a blockchain rewriting mechanism called RBCVC, which combines chameleon hash and voting-based consensus. Thirdly, we conducted theoretical analysis and experimental evaluations of StarCross. The results indicate that compared to existing solutions, StarCross achieves better scalability and security in S-IoT. StarCross’s TPS increased to 260.9% of the baseline, while the transaction confirmation latency decreased by 79.7%.