Privacy Challenges Research Articles

Privacy and Security Challenges in IoT Deployments

The IoT means the Internet of Things – the integration of things and systems in the digital world across numerous industries. As has been seen we can opened up an exponential wave of efficiency, automation and innovation in the domain of IoT while at the same time we have exposed an infinite number of privacy and security threats that could severely jeopardize individuals, organizations, or nations. This paper thus critically analyses the status and trends of IoT privacy and security threats focusing more on the most common threats as well as impacts arising from their exploitation. From the analysis of the most reported case studies in this paper, the kind of recent IoT security breaches and the efforts made to address these are revealed. Moreover, the paper provides guidelines on imposing improvements to IoT systems security by persuading the implementation of global and inclusive security that targets prior and emergent threats. At the same time, this work also synthesises the most current advances in the field of IoTs security research, as well as outline specific directions for future research, which is also evidence of the necessity for Iots stakeholders to contin ue the improvement and collaboration to protect the future of IoTs environments.

Evaluating the Ethical Practices in Developing AI and Ml Systems in Tanzania

Artificial Intelligence (AI) and Machine Learning (ML) present transformative opportunities for sectors in developing countries like Tanzania that were previously hindered by manual processes and data inefficiencies. Despite these advancements, the ethical challenges of bias, fairness, transparency, privacy, and accountability are critical during AI and ML system design and deployment. This study explores these ethical dimensions from the perspective of Tanzanian IT professionals, given the country's nascent AI landscape. The research aims to understand and address these challenges using a mixed-method approach, including case studies, a systematic literature review, and critical analysis. Findings reveal significant concerns about algorithm bias, the complexity of ensuring fairness and equity, transparency and explainability, which are crucial for promoting trust and understanding among users, and heightened privacy and security risks. The study underscores the importance of integrating ethical considerations throughout the development lifecycle of AI and ML systems and the necessity of robust regulatory frameworks. Recommendations include developing targeted regulatory guidelines, providing comprehensive training for IT professionals, and fostering public trust through transparency and accountability. This study underscores the importance of ethical AI and ML practices to ensure responsible and equitable technological development in Tanzania.

Efficient Lightweight Blockchain with Hybridized Consensus Algorithm for IoT Networks

Blockchain (BC) technology has attracted a lot of interest due to its excellent security and privacy features as well as its immutability. While BC can address security and privacy challenges in IoT, it is often computationally expensive, suffers from limited scalability, and introduces bandwidth overheads and delays, making it unsuitable for IoT applications. This paper proposes a novel lightweight and scalable blockchain (LIGHT-SB) designed specifically for IoT environments. In a smart IoT ecosystem, low-resource devices benefit from a centralized manager that handles communication keys and manages requests. LIGHT-SB enhances decentralization while maintaining robust privacy and security through a hybrid consensus algorithm combining permissionless proof-of-capacity and permissioned Byzantine fault tolerance (PoC-PBFT). Secure message delivery is achieved using SHA-256 and RSA cryptographic techniques. IoT device authentication, communication, and storage are safeguarded within the network. Distributed throughput management is implemented to monitor and adjust BC usage, ensuring it remains efficient. Qualitative evaluations demonstrate that LIGHT-SB effectively mitigates several security threats.

Enhancing E‐Healthcare Data Privacy Through Efficient Dual Signature on Twisted Edwards Curves Encryption Decryption

ABSTRACTThis article introduces a scheme designed to ensure the integrity and privacy of e‐healthcare data. The proposed scheme combines a dual signature approach with EdDSA (Edwards‐curve Digital Signature Algorithm). It is important to note that while dual signatures may appear similar to double signatures, they differ significantly. Dual signatures involve coupling two distinct values of diverse natures to securely anonymise them to various entities. Recent research has demonstrated that elliptic curve cryptography (ECC) is better suited for resource‐constrained or lightweight devices than RSA (Rivest–Shamir–Adleman). EdDSA, an enhancement of ECDSA, offers efficient and affordable signature generation. However, EdDSA signature verification is a computationally intensive task that may not be feasible for e‐healthcare devices. Nevertheless, edge computing devices lack operational constraints and can easily handle this verification process. These characteristics make our proposal an elegant, robust, and efficient strategy for securing e‐healthcare systems and addressing the challenges of integrity and privacy. Results show that the proposed algorithm performs better in terms of key generation, signing time, verification time, encryption/decryption time and overall time as compared to the state‐of‐the‐art.

From Barista to Bytes: How Starbucks Brewed a Digital Revolution

This study examines Starbucks' digital transformation journey, analyzing its strategies, implementation, and impact on performance and competitive positioning. Using a qualitative case study approach, the article explores how Starbucks leveraged an interconnected ecosystem of mobile applications, artificial intelligence, cloud computing, and data analytics to enhance customer engagement and optimize operations. The research investigates how these technologies synergistically contribute to Starbucks' "Digital Flywheel" framework, driving personalization, streamlining operations, and creating a seamless omnichannel experience. Drawing on company reports, academic literature, and industry analyses, this study provides a comprehensive view of Starbucks' digital initiatives and their collective impact. Findings reveal that Starbucks' success stems from a holistic approach to digital transformation, encompassing technological implementation, organizational restructuring, and cultural change. The study critically examines the effectiveness of the "Digital Flywheel" framework, comparing it with similar models in digital transformation literature. While highlighting the significant impact of these initiatives on Starbucks' financial performance, customer engagement, and operational efficiency, the research also explores challenges in data privacy, technology investment costs, and potential risks associated with digital transformation. The study provides a nuanced analysis of how Starbucks navigated these challenges, offering valuable insights for practitioners. This research contributes to the growing literature on digital transformation in retail, offering a balanced perspective on both the opportunities and challenges of digital innovation. By examining Starbucks' journey, the study critically assesses how traditional retailers can adapt to the digital age, while acknowledging the variability in outcomes across different sectors.

Personalized federated learning for buildings energy consumption forecasting

Buildings' energy consumption forecasting is critical for energy saving and building maintenance. However, most studies only focus on centralized learning of one dataset, which ignores the data privacy and data shortage issue. Meanwhile, the difference in energy data distributions from many buildings causes difficulties in training a good machine learning model. Although these two challenges of data privacy and data heterogeneity could be resolved through personalized federated learning algorithms to some degree, there is still a lack of investigation into applying these algorithms to building energy data analytics. Besides using existing personalized federated learning algorithms, we design a new deep learning model through a mixture of experts to support personalization for heterogeneous data distribution. This new design is the first trial to tackle the data heterogeneity through ensemble architecture in federated load forecasting. Extensive experiments are conducted to evaluate the effectiveness of our proposed model with different training algorithms. The results show that our proposed method outperforms other state-of-the-art models in energy forecasting accuracies by 10% to 40% across the buildings' energy data from university campuses.

Information security in remote work: Strategies and challenges in a post-pandemic world

The increasing prevalence of remote work, accelerated by the COVID-19 pandemic, has brought significant information security challenges to organizations. While remote work offers numerous benefits such as flexibility and increased productivity, it also introduces unique risks, particularly in safeguarding sensitive data. The absence of traditional office security measures and reliance on potentially insecure home and public networks heighten these risks. Organizations must implement robust strategies, including the use of Virtual Private Networks (VPNs) and multi-factor authentication (MFA), to ensure secure communications and access to corporate systems. Additionally, the protection of devices used in remote work is critical, necessitating clear security policies, regular updates, and employee training on recognizing threats like phishing and malware. Studies by Kolomoets (2022) and Alsayfi and Alsirhani (2023) highlight the importance of addressing the increased risks associated with remote work, particularly the potential for data breaches due to the lack of direct access to comprehensive security controls. These studies recommend best practices such as secure password management and regular employee training to mitigate these threats. Tanriverdi and Metin (2021) emphasize the need for renewed focus on security awareness and behavior as remote work becomes the norm. Furthermore, Rakha (2023) explores the legal implications and best practices for maintaining cybersecurity in remote settings, while Livshitz (2022) focuses on data privacy challenges, providing insights into regulatory compliance and security audits. In conclusion, safeguarding information security in remote work environments requires a holistic approach that integrates technology, continuous education, and stringent security policies. Organizations must prioritize these efforts to protect sensitive data and maintain privacy in an increasingly remote workforce.

Privacy‐preserving collaboration in blockchain‐enabled IoT: The synergy of modified homomorphic encryption and federated learning

SummaryThe proliferation of network devices capable of gathering, transmitting, and receiving data over the Internet has spurred the widespread adoption of Internet of Things (IoT) devices, particularly in resource‐oriented applications. Integrating blockchain, IoT, homomorphic encryption, and federated learning requires a balance between computational requirements and real‐time performance. Secure key management is crucial to maintain data privacy and integrity. Compliance with privacy regulations requires careful implementation of privacy‐preserving mechanisms in blockchain‐enabled IoT environments, which can be subjected to various attacks. Addressing these challenges requires interdisciplinary expertise, research, and innovation to develop more efficient and effective privacy‐preserving techniques tailored to the unique characteristics of such environments. This research introduces the Modified Homomorphic Encryption Federated‐based Adaptive Hybrid Dandelion Search (MHEF‐AHDS) algorithm as an effective framework to enhance security in blockchain‐enabled IoT systems. The amalgamation of Modified Homomorphic Encryption (MHE) and Federated Learning (FL) constitutes a potent alliance that addresses privacy concerns within collaborative and decentralized machine learning environments. This facilitates secure and adaptable data collaboration, effectively mitigating privacy risks associated with sensitive information. The integration of quantum machine learning into security applications presents an exciting opportunity for distinctive progress and innovation. Within this work, the Adaptive Hybrid Dandelion optimization algorithm, featuring an Initial search strategy, is employed for hyperparameter optimization thereby elevating the performances of the proposed MHEF‐AHDS method. Furthermore, the integration of smart contracts and Blockchain‐based IoT enhances the overall security of the proposed method. MHEF‐AHDS comprehensively tackles privacy, security, and scalability challenges through robust security measures and privacy enhancements. The performance evaluation of the MHEF‐AHDS method encompasses a thorough analysis based on key metrics such as throughput, latency, scalability, energy consumption, accuracy, precision, recall, and f1‐score. Comparative assessments against existing methods are conducted to gauge the effectiveness of the proposed method in addressing security, privacy, and scalability concerns.

Intelligent Health Monitoring Device Data Analysis: From Data Collection to Health Insights

The integration of smart wearable and home health monitoring devices marks a new epoch in health management, offering deep insights through real-time data. This study seeks to convert this collected health data into actionable personalized assessments and recommendations, enhancing health management efficacy. Utilizing literature review, case analysis, and data analysis, the research initially describes smart devices’ capabilities and data gathering techniques. It then concentrates on data processing methodologies, statistical analysis, and machine learning, examining the translation of these into usable health insights. The study reveals that smart devices offer continuous health data, enabling the identification of individual trends and risks through data analysis. Personalized assessments can boost engagement and adherence to health plans. Yet, it acknowledges challenges in data privacy, technological synthesis, and user adoption, proposing solutions like fortified security, interface optimization, and enhanced analytical accuracy. In conclusion, the research highlights the potential of data analysis in advancing personalized health management and recommends future explorations, such as refining algorithms, bolstering privacy safeguards, and setting regulatory benchmarks.

Artificial intelligence in the United Arab Emirates public sector: a systematic literature review

<p><span lang="EN-US">This systematic literature review examines United Arab Emirates (UAE) public sector artificial intelligence (AI) use, impact, and challenges. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol, 20 relevant Scopus articles were selected for the study. Data from selected articles were used to analyse AI's use, benefits, and drawbacks in the UAE's public sector. Quality assessment was done throughout the review process. The results showed that AI is being used more in the UAE's public sector to improve efficiency, cost savings, decision-making, and service delivery. The review also found data, privacy, security, technical, infrastructure, AI, and user challenges. Publication bias and the lack of AI studies in the UAE's public sector limit the study. The findings have major implications for policy and practice, emphasising the need for AI strategies and UAE-specific solutions.</span></p>

An Adversarial Machine Learning Based Approach for Privacy Preserving Face Recognition in Distributed Smart City Surveillance

Smart cities rely heavily on surveillance cameras for urban management and security. However, the extensive use of these cameras also raises significant concerns regarding data privacy. Unauthorized access to facial data captured by these cameras and the potential for misuse of this data poses serious threats to individuals’ privacy. Current privacy preservation solutions often compromise data usability with noise application-based approaches and vulnerable centralized data handling settings. To address these privacy challenges, we propose a novel approach that combines Adversarial Machine Learning (AML) with Federated Learning (FL). Our approach involves the use of a noise generator that perturbs surveillance data right from the source before they leave the surveillance cameras. By exclusively training the Federated Learning model on these perturbed samples, we ensure that sensitive biometric features are not shared with centralized servers. Instead, such data remains on local devices (e.g., cameras), thereby ensuring that data privacy is maintained. We performed a thorough real-world evaluation of the proposed method and achieved an accuracy of around 99.95% in standard machine learning settings. In distributed settings, we achieved an accuracy of around 96.24% using federated learning, demonstrating the practicality and effectiveness of the proposed solution.11The code is available at: https://github.com/farah-wahida/Privacy-Preserving-Face-Recognition-in-Distributed-Smart-City-Surveillance.

Machine Learning-Driven Reconfigurable EONs with Neuromorphic Computing for Network Slicing and On-Demand Service Provisioning: A Review and Survey

Abstract: This paper delves into the substantial potential of integrating advanced technologies within Reconfigurable Elastic Optical Networks (REONs). By leveraging machine learning and neuromorphic computing, these networks can significantly enhance performance, scalability, and efficiency. Machine learning models facilitate dynamic resource management, allowing for the on-demand reconfiguration of optical networks to improve service provisioning and maintain high Quality of Service (QoS). Neuromorphic processors further boost network-slicing capabilities, optimizing bandwidth management and enabling the creation of customized virtual networks. Additionally, the incorporation of neuromorphic computing into REONs contributes to substantial energy savings, a critical factor for sustainable network operations. Techniques such as differential privacy and secure multi-party computation effectively address security and privacy challenges within optical networks. Future research should focus on developing scalable architectures, formulating energy-efficient algorithms, and designing solutions tailored to specific applications to maximize the potential of REONs. In summary, the integration of advanced computing techniques within REONs promises to revolutionize network management and service delivery, equipping future networks to deliver exceptional performance, scalability, and adaptability, thus meeting the evolving demands of modern communication environments.

Real-Time Surveillance with AI: A Comprehensive Approach to Security and Monitoring

The last ten years have been quite phenomenal for artificial intelligence and computer vision. For the past few years, it was unstoppable, with outstanding breakthroughs that moved towards advanced real-time surveillance systems-that wholesome approach to real-time surveillance through the power of AI in technological advancement for accuracy, scalability, and responsiveness. The system integrates facial recognition, object detection, and anomaly detection algorithms to give a very stable, adaptive response to the various facets of surveillance needs, such as public safety, institutional monitoring, and private security. Through deep learning models extracted from comprehensive datasets, the system is able to identify known individuals in real time and trace the movements and unusual behaviors of these people. A discussion of challenges in data privacy, computational efficiency, and false positives goes hand in hand with an in-depth analysis of system architecture, which includes thorough hardware and software configuration descriptions. The experimental results show the potential of the system in real-world environments as an alternate scalable solution for future security infrastructures.

Enhancing security and risk management with predictive analytics: A proactive approach

and interconnected world. This paper explores the transformative potential of predictive analytics in enhancing security and risk management by shifting from reactive to proactive strategies. Examining the theoretical foundations and application areas of predictive analytics, the paper highlights how organizations can anticipate and mitigate risks across various domains, including cybersecurity, fraud detection, and supply chain management. The benefits of predictive analytics, such as early threat detection and optimized resource allocation, are discussed alongside data quality, privacy, and model interpretability challenges. Additionally, emerging trends, such as artificial intelligence, real-time data analytics, and blockchain technology, are key drivers shaping the future of predictive analytics in risk management. The paper concludes by emphasizing organizations' need to integrate predictive analytics into their risk management frameworks to enhance resilience and ensure long-term success in a rapidly evolving risk landscape.

Privacy-preserving estimation of electric vehicle charging behavior: A federated learning approach based on differential privacy

With the popularity of connected electric vehicles, the openness and sharing of charging data between stakeholders allows a more accurate estimation of charging behavior, which is valuable for optimizing energy systems and facilitating travel convenience. However, to enable such an effective mechanism, the challenge of data security and privacy should be addressed. Federated learning in the vehicular network is appealing for utilizing individual vehicle data while preserving data privacy. We propose an improved local differential privacy-based federated learning approach for modeling charging session prediction problems while preserving user privacy against the threat from a honest-but-curious server. In this approach, all vehicles, within the coordination of a cloud server, collaboratively establish a global regression network through parameter exchange. Meanwhile, the servers may belong to third-party model owners and can be semi-honest when inferring private information on the collected model parameters. Hence, local differential privacy is adopted to perturb the parameters. Additionally, a combination of local and global models via elastic synchronization is proposed to improve the accuracy of the learned noisy global model. Through the test on a real data set, the results show the superiority of the proposed algorithm over traditional noisy federated learning methods. Furthermore, the practical value of the proposed method is validated with a real-world charging case. Such an accurate charging session prediction service for electric vehicle drivers facilitates charging and travel convenience in the green transportation world.

Emerging South African smart cities: Data security and privacy risks and challenges

Background: Smart cities leverage advanced technologies such as the Internet of Things (IoT), fifth generation (5G) networks, and data analytics to enhance citizens’ quality of life, focussing on creating efficient, functional, and eco-friendly urban environments. While these initiatives offer significant benefits, there are ongoing concerns about data security and privacy.Objectives: This article investigates the data security and privacy risks and challenges in emerging South African smart cities. The objectives are to identify these risks, assess the effectiveness of current security measures, and contextualise these vulnerabilities within the South African and global contexts.Method: A qualitative approach was adopted, involving virtual interviews with 20 Subject Matter Experts (SMEs) through purposive and snowball sampling. The raw data were thematically analysed, revealing significant themes.Results: Emerging South African smart cities face several risks and challenges, including poor governance, a shortage of skills, a lack of awareness and training, insufficient funding, and a combination of these factors.Conclusion: The research highlights the importance of safeguarding individuals’ data and privacy in the context of smart cities, advocating the need for proactive measures to address these concerns.Contribution: This article promotes interdisciplinary dialogue, leading to more comprehensive solutions. In addition, exploring data security and privacy in emerging smart cities aids in understanding the implications of information management practices. Although the article focuses on emerging South African smart cities, the challenges discussed have global relevance.

Data privacy and cybersecurity challenges in the digital transformation of the banking sector

In the digital transformation of the banking sector, incorporating advanced technologies such as cloud computing, big data analytics, artificial intelligence, and blockchain has revolutionized financial services. However, this rapid digitalization brings significant data privacy and cybersecurity challenges. This study investigates the challenges banks have maintaining data privacy and cybersecurity while implementing new technologies, how they perceive these challenges, and what steps they take to reduce the risks involved. This qualitative study uses thematic analysis to examine interviews conducted with IT specialists in the banking sector. NVivo 14 software is employed to identify key themes and patterns related to the challenges, perceptions, and strategies regarding data privacy and cybersecurity in technology adoption. The findings reveal that the primary challenges faced by banks include integrating legacy systems, evolving compliance management, managing vendor risks, maintaining customer confidence, and mitigating emerging risks. Banks perceive robust data privacy and cybersecurity as critical for competitive advantage, regulatory compliance, and customer trust. Strategies include robust access controls, continuous threat monitoring, employee training, regulatory compliance with governance frameworks, and data encryption. This study provides original insights into the specific challenges and strategies related to data privacy and cybersecurity faced by banks. It contributes to the existing literature by highlighting the unique context of the banking sector and employing qualitative analysis to uncover nuanced perceptions and practices of IT specialists.

Nurses' perspectives on privacy and ethical concerns regarding artificial intelligence adoption in healthcare

BackgroundWith the increasing integration of artificial intelligence (AI) technologies into healthcare systems, there is a growing emphasis on privacy and ethical considerations. Nurses, as frontline healthcare professionals, are pivotal in-patient care and offer valuable insights into the ethical implications of AI adoption. ObjectivesThis study aimed to explore nurses' perspectives on privacy and ethical concerns associated with the implementation of AI in healthcare settings. MethodsWe employed Van Manen's hermeneutic phenomenology as the qualitative research approach. Data were collected through purposive sampling from the December 7, 2023 to the January 15, 2024, with interviews conducted in Bengali. Thematic analysis was utilized following member checking and an audit trail. ResultsSix themes emerged from the research findings: Ethical dimensions of AI integration, highlighting complexities in incorporating AI ethically; Privacy challenges in healthcare AI, revealing concerns about data security and confidentiality; Balancing innovation and ethical practice, indicating a need to reconcile technological advancements with ethical considerations; Human touch vs. technological progress, underscoring tensions between automation and personalized care; Patient-centered care in the AI era, emphasizing the importance of maintaining focus on patients amidst technological advancements; and Ethical preparedness and education, suggesting a need for enhanced training and education on ethical AI use in healthcare. ConclusionsThe findings underscore the importance of addressing privacy and ethical concerns in AI healthcare development. Nurses advocate for patient-centered approaches and collaborate with policymakers and tech developers to ensure responsible AI adoption. Further research is imperative for mitigating ethical challenges and promoting ethical AI in healthcare practice.

Secure Extraction of Personal Information from EHR by Federated Machine Learning.

Secure extraction of Personally Identifiable Information (PII) from Electronic Health Records (EHRs) presents significant privacy and security challenges. This study explores the application of Federated Learning (FL) to overcome these challenges within the context of French EHRs. By utilizing a multilingual BERT model in an FL simulation involving 20 hospitals, each represented by a unique medical department or pole, we compared the performance of two setups: individual models, where each hospital uses only its own training and validation data without engaging in the FL process, and federated models, where multiple hospitals collaborate to train a global FL model. Our findings demonstrate that FL models not only preserve data confidentiality but also outperform the individual models. In fact, the Global FL model achieved an F1 score of 75,7%, slightly comparable to that of the Centralized approach at 78,5%. This research underscores the potential of FL in extracting PIIs from EHRs, encouraging its broader adoption in health data analysis.

Security and Privacy in Physical Activity Chatbots on Social Media: A Scoping Review.

Social media physical activity chatbots use both chatbots and social media platforms for physical activity promotion and, thus, could face privacy and security challenges inherent in both technologies. This study aims to provide an overview of physical activity chatbot interventions delivered via social media platforms, specifically focusing on security and privacy measures. We conducted a scoping review on this topic across 4 databases: PubMed, PsycINFO, ACM Digital Library, and IEEE Xplore. We extracted and summarised information on the author, population, country of study, social media platform, intervention, data processed (i.e. gathered or stored), and security/privacy measures. Out of 1299 identified articles, 12 were included in the analysis reporting about 9 different chatbots. Although all chatbots applied data processing methods, only a few considered anonymisation. One paper stated compliance with the General Data Protection Regulations. Other studies enforced some verification procedures before chatbot use. Current research fails to adequately report security considerations in social media physical activity chatbot design. However, integration of chatbots into social media platforms seems to be declining, possibly due to security concerns.