Ensure Data Privacy Research Articles

Emotions Matter: A Systematic Review and Meta-Analysis of the Detection and Classification of Students’ Emotions in STEM during Online Learning

In recent years, the rapid growth of online learning has highlighted the need for effective methods to monitor and improve student experiences. Emotions play a crucial role in shaping students’ engagement, motivation, and satisfaction in online learning environments, particularly in complex STEM subjects. In this context, sentiment analysis has emerged as a promising tool to detect and classify emotions expressed in textual and visual forms. This study offers an extensive literature review using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) technique on the role of sentiment analysis in student satisfaction and online learning in STEM subjects. The review analyses the applicability, challenges, and limitations of text- and facial-based sentiment analysis techniques in educational settings by reviewing 57 peer-reviewed research articles out of 236 articles, published between 2015 and 2023, initially identified through a comprehensive search strategy. Through an extensive search and scrutiny process, these articles were selected based on their relevance and contribution to the topic. The review’s findings indicate that sentiment analysis holds significant potential for improving student experiences, encouraging personalised learning, and promoting satisfaction in the online learning environment. Educators and administrators can gain valuable insights into students’ emotions and perceptions by employing computational techniques to analyse and interpret emotions expressed in text and facial expressions. However, the review also identifies several challenges and limitations associated with sentiment analysis in educational settings. These challenges include the need for accurate emotion detection and interpretation, addressing cultural and linguistic variations, ensuring data privacy and ethics, and a reliance on high-quality data sources. Despite these challenges, the review highlights the immense potential of sentiment analysis in transforming online learning experiences in STEM subjects and recommends further research and development in this area.

Federated learning with hyper-parameter optimization

Federated Learning is a new approach for distributed training of a deep learning model on data scattered across a large number of clients while ensuring data privacy. However, this approach faces certain limitations, including a longer convergence time compared to typical deep learning models. Existing federated optimization algorithms often employ the same hyper-parameters for all clients, disregarding potential system heterogeneity and varying local data availability, which contributes to an even longer convergence time and more communication rounds. To address this challenge, we propose FedHPO, a new federated optimization algorithm that adaptively modifies hyper-parameters of each client’s local model during training, such as learning rate and epochs. This adaptability facilitates quicker convergence of the client’s local model, which in turn helps the global model converge faster, consequently reducing overall convergence time and the required communication rounds. In addition, FedHPO does not require any additional complexity since each client adjusts hyper-parameters independently based on the training results obtained in each epoch. In our evaluation, we compare FedHPO with other algorithms, such as FedAVG, FedAVGM, FedProx, and FedYogi, using both IID and non-IID distributed datasets. The results demonstrate the promising outcomes of FedHPO, showcasing reduced convergence time and fewer required communication rounds in comparison to alternative algorithms.

Enhancing the Security and Privacy in the IoT Supply Chain Using Blockchain and Federated Learning with Trusted Execution Environment

Federated learning has emerged as a promising technique for the Internet of Things (IoT) in various domains, including supply chain management. It enables IoT devices to collaboratively learn without exposing their raw data, ensuring data privacy. However, federated learning faces the threats of local data tampering and upload process attacks. This paper proposes an innovative framework that leverages Trusted Execution Environment (TEE) and blockchain technology to address the data security and privacy challenges in federated learning for IoT supply chain management. Our framework achieves the security of local data computation and the tampering resistance of data update uploads using TEE and the blockchain. We adopt Intel Software Guard Extensions (SGXs) as the specific implementation of TEE, which can guarantee the secure execution of local models on SGX-enabled processors. We also use consortium blockchain technology to build a verification network and consensus mechanism, ensuring the security and tamper resistance of the data upload and aggregation process. Finally, each cluster can obtain the aggregated parameters from the blockchain. To evaluate the performance of our proposed framework, we conducted several experiments with different numbers of participants and different datasets and validated the effectiveness of our scheme. We tested the final global model obtained from federated training on a test dataset and found that increasing both the number of iterations and the number of participants improves its accuracy. For instance, it reaches 94% accuracy with one participant and five iterations and 98.5% accuracy with ten participants and thirty iterations.

Breast Cancer Detection Based on Convolutional Neural Networks

Abstract: Breast cancer remains a critical health concern, demanding early detection and accurate classification for effective treatment. In this research, we conduct a comparative study between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We begin by curating a comprehensive dataset of breast cancer images and apply appropriate data preprocessing techniques for optimal model input. The dataset is divided into training, validation, and testing sets to evaluate model performance. The CNN model is constructed with multiple convolutional and pooling layers, followed by fully connected layers for classification. Meanwhile, the DenseNet121, a powerful pre-trained model, is fine-tuned for breast cancer detection. Through rigorous evaluation, we assess both models using metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. Our results demonstrate that the DenseNet121 outperforms the custom CNN model, achieving higher accuracy and reliability in identifying and classifying breast cancer. To ensure wider accessibility, we integrate the superior DenseNet121 model into a user-friendly web-based interface using Python Flask. This interface empowers medical professionals and the general public to perform real-time breast cancer predictions with ease. Ethical considerations are paramount, ensuring data privacy, security, and transparency in all model predictions. In conclusion, our comparative study highlights the superiority of the pre-trained DenseNet121 model over a custom CNN for breast cancer detection and classification. By leveraging advanced deep learning techniques and a user-friendly interface, our research contributes to improved breast cancer diagnosis and patient care on a broader scale

Artificial Intelligence and Machine Learning in Pharmacological Research: Bridging the Gap Between Data and Drug Discovery.

Artificial intelligence (AI) has transformed pharmacological research through machine learning, deep learning, and natural language processing. These advancements have greatly influenced drug discovery, development, and precision medicine. AI algorithms analyze vast biomedical data identifying potential drug targets, predicting efficacy, and optimizing lead compounds. AI has diverse applications in pharmacological research, including target identification, drug repurposing, virtual screening, de novo drug design, toxicity prediction, and personalized medicine. AI improves patient selection, trial design, and real-time data analysis in clinical trials, leading to enhanced safety and efficacy outcomes. Post-marketing surveillance utilizes AI-based systems to monitor adverse events, detect drug interactions, and support pharmacovigilance efforts. Machine learning models extract patterns from complex datasets, enabling accurate predictions and informed decision-making, thus accelerating drug discovery. Deep learning, specifically convolutional neural networks (CNN), excels in image analysis, aiding biomarker identification and optimizing drug formulation. Natural language processing facilitates the mining and analysis of scientific literature, unlocking valuable insights and information. However, the adoption of AI in pharmacological research raises ethical considerations. Ensuring data privacy and security, addressing algorithm bias and transparency, obtaining informed consent, and maintaining human oversight in decision-making are crucial ethical concerns. The responsible deployment of AI necessitates robust frameworks and regulations. The future of AI in pharmacological research is promising, with integration with emerging technologies like genomics, proteomics, and metabolomics offering the potential for personalized medicine and targeted therapies. Collaboration among academia, industry, and regulatory bodies is essential for the ethical implementation of AI in drug discovery and development. Continuous research and development in AI techniques and comprehensive training programs will empower scientists and healthcare professionals to fully exploit AI's potential, leading to improved patient outcomes and innovative pharmacological interventions.

CP-ABE Unveiled: A Path to Fine-Tuned Cloud Data Security

Cloud computing has revolutionized data storage and processing, but it has also introduced significant challenges in ensuring data privacy and access control. In this research paper, we explore and understand the potential of Ciphertext Policy Attribute-Based Encryption (CP-ABE) as a cryptographic solution for enhancing cloud security through fine-grained access control. Our exploration begins with a comprehensive analysis of CP-ABE, including its architecture, components, and encryption/decryption processes. We delve into the security aspects of CP-ABE, evaluating its strengths, advantages, and vulnerabilities. We conduct a thorough performance evaluation, analyzing its computational overhead and comparingits efficiency with other encryption techniques commonly used in the cloud. Our findings indicate that CP-ABE offers fine-grained access control and data confidentiality, empowering data owners to enforce complex access policies. However, challenges related to attribute authority trust, key escrow, and policy complexity must be addressed to ensure its robustness in real-world deployments.Ciphertext Policy Attribute-Based Encryption (CP-ABE) introduces a hopeful method for dealing with the challenges related to controlling who can access information and keeping data private in cloud computing.Our research contributes valuable insights into CP-ABE's security, performance, and practical applications.

Data management and protection in occupational and environmental exposome research - A case study from the EU-funded EXIMIOUS project

Within collaborative projects, such as the EU-funded Horizon 2020 EXIMIOUS project (Mapping Exposure-Induced Immune Effects: Connecting the Exposome and the Immunome), collection and analysis of large volumes of data pose challenges in the domain of data management, with regards to both ethical and legal aspects. However, researchers often lack the right tools and/or accurate understanding of the ethical/legal framework to independently address such challenges. With the guidance and support within and between the partner institutes (the researchers and the ethical and legal teams) in the EXIMIOUS project, we have been able to understand and solve most challenges during the first two project years. This has fed into the development of a Data Management Plan and the establishment of data management platforms in accordance with the ethical and legal framework laid down by the EU and the different national regulations of the partners involved. Through this elaborate exercise, we have acquired tools which allow us to make our research data FAIR (Findable, Accessible, Interoperable, and Reusable), while at the same time ensuring data privacy and security (GDPR compliant). Herein we share our experience of creating and managing the data workflow through an open research communication, with the aim of helping other researchers build their data management framework in their own projects. Based on the measures adopted in EXIMIOUS to ensure FAIR data management, we also put together a checklist “DMP CHECK” containing a series of recommendations based on our experience.

The Future of Web

The future of web is a dynamic and ever-evolving landscape that continues to shape how information is discovered, analysed, and shared. This research paper presents a comprehensive exploration of the emerging trends and potential implications that will drive the evolution of web research in the coming years. Advances in web crawling and data collection technologies are opening new possibilities for researchers to access and analyse vast amounts of information from an increasingly diverse range of online sources. Coupled with the integration of artificial intelligence and natural language processing, web research is poised to become more efficient and insightful, enabling researchers to extract valuable insights from unstructured data. The emergence of the Semantic Web introduces standardized formats such as RDF and OWL, enhancing data interoperability and allowing for more precise searches and cross-disciplinary connections. However, this transformation also raises ethical concerns surrounding user privacy, data ownership, and the responsible use of AI-driven decision-making tools, which necessitates careful consideration by researchers and policymakers. As web research continues to influence knowledge dissemination, open access initiatives, preprint repositories, and collaborative platforms are reshaping the traditional publishing model. Real-time sharing of research findings becomes increasingly common, necessitating new norms of collaboration and knowledge exchange among the academic community. Moreover, the integration of virtual and augmented reality technologies in web research offers exciting opportunities for data visualization, making complex datasets more accessible and engaging. Virtual reality environments may become common spaces for interdisciplinary collaboration, enabling researchers from diverse fields to collaborate seamlessly. However, along with the prospects, challenges lie ahead. Ensuring data privacy and security in an interconnected web environment will be crucial. Additionally, developing guidelines for responsible AI research and addressing biases in AI algorithms will be essential for maintaining ethical practices in web research

Influence of online E-commerce interaction on consumer satisfaction based on big data algorithm

With the rapid development of the times, people have entered the era of intelligence, and the application of big data algorithms is becoming increasingly widespread. The satisfaction of consumers in online shopping is closely related to communication and interaction during shopping. Based on this, this article studies the impact of online e-commerce interaction on consumer satisfaction based on big data algorithms. This article introduces the mediating variable of consumer satisfaction from the perspective of interaction, constructs a model between interaction and trust, and studies the internal impact mechanism of online interaction on consumer satisfaction in online shopping. This article takes the JD interactive shopping platform as the research object, and analyzes and explores the target consumer satisfaction of the women's clothing interactive shopping platform. Analyzed the impact of interaction on merchant qualifications and service satisfaction evaluations, store size, and logistics of purchased goods. The research results indicate that the normalization coefficients of the H1a and H1b pathways are 0.131 and 0.118, respectively, which are slightly smaller, indicating that the impact of perceived risk on consumer satisfaction is not significant. Meanwhile, the CR in H1 is a positive number and the direction of influence is positive, which is contrary to the assumption. Therefore, it is necessary to calibrate the initial model. After correction, the GFI value is 0.816, AGFI value is 0.825, RMSEA value is 0.042, TFI value is 0.930, CFI value is 0.955, PGFI value is 0.718, and PNFI value is 0.810. The degree is within an acceptable range. Therefore, when implementing interactive shopping, e-commerce companies need to create a good shopping environment for the implementation of interactive activities between sellers and customers. The impact of online e-commerce interaction based on big data algorithms on consumer satisfaction is a hot topic. Personalized recommendations can improve consumer satisfaction and loyalty, but data privacy and security issues are also receiving increasing attention. In addition, it is also necessary to consider the fairness and bias issues of the algorithm, as well as the transparency issues of data analysis and decision-making. On the premise of ensuring data privacy and security, it is necessary to improve the fairness and transparency of algorithms to improve consumer satisfaction and trust, and achieve sustainable development.

Empowering Pharmacovigilance: Unleashing the Potential of Generative AI in Drug Safety Monitoring

Pharmacovigilance plays a crucial role in ensuring drug safety and promoting patient well-being throughout the life cycle of medicinal products. However, this field faces several challenges, including underreporting of adverse events, data quality issues, and the complexity of signal detection in large datasets. To address these challenges and enhance drug safety monitoring, there is a growing interest in harnessing the potential of generative artificial intelligence (AI) techniques. This article explores the applications and implications of generative AI in pharmacovigilance. It provides an overview of popular generative models and their working principles, highlighting their ability to analyse drug databases, medical literature, and real-world data sources to identify drug-drug interactions, adverse events, and potential safety signals. Moreover, it emphasizes the importance of human validation and expert oversight in interpreting and acting on the insights generated by generative AI algorithms. The integration of generative AI with traditional pharmacovigilance methods creates a synergistic approach, combining the computational power of AI with human expertise. This integration can lead to improved signal detection, efficient case report generation, proactive risk assessment, and optimized resource allocation. Additionally, the article addresses challenges related to data quality, interpretability, and model validation in generative AI applications, emphasizing the need for standardized protocols and collaborative efforts among stakeholders. Overall, the potential of generative AI in pharmacovigilance is vast. By leveraging its capabilities, we can enhance drug safety monitoring, facilitate early detection of adverse events, and improve patient outcomes. However, it is crucial to address ethical considerations, ensure data privacy, and maintain human oversight to foster responsible and effective implementation of generative AI in pharmacovigilance practices.

Empowering Patient Similarity Networks through Innovative Data-Quality-Aware Federated Profiling.

Continuous monitoring of patients involves collecting and analyzing sensory data from a multitude of sources. To overcome communication overhead, ensure data privacy and security, reduce data loss, and maintain efficient resource usage, the processing and analytics are moved close to where the data are located (e.g., the edge). However, data quality (DQ) can be degraded because of imprecise or malfunctioning sensors, dynamic changes in the environment, transmission failures, or delays. Therefore, it is crucial to keep an eye on data quality and spot problems as quickly as possible, so that they do not mislead clinical judgments and lead to the wrong course of action. In this article, a novel approach called federated data quality profiling (FDQP) is proposed to assess the quality of the data at the edge. FDQP is inspired by federated learning (FL) and serves as a condensed document or a guide for node data quality assurance. The FDQP formal model is developed to capture the quality dimensions specified in the data quality profile (DQP). The proposed approach uses federated feature selection to improve classifier precision and rank features based on criteria such as feature value, outlier percentage, and missing data percentage. Extensive experimentation using a fetal dataset split into different edge nodes and a set of scenarios were carefully chosen to evaluate the proposed FDQP model. The results of the experiments demonstrated that the proposed FDQP approach positively improved the DQ, and thus, impacted the accuracy of the federated patient similarity network (FPSN)-based machine learning models. The proposed data-quality-aware federated PSN architecture leveraging FDQP model with data collected from edge nodes can effectively improve the data quality and accuracy of the federated patient similarity network (FPSN)-based machine learning models. Our profiling algorithm used lightweight profile exchange instead of full data processing at the edge, which resulted in optimal data quality achievement, thus improving efficiency. Overall, FDQP is an effective method for assessing data quality in the edge computing environment, and we believe that the proposed approach can be applied to other scenarios beyond patient monitoring.

SNN-PPRL: A secure record matching scheme based on siamese neural network

With the rapid development of IoT, data from various devices is increasing exponentially. To better apply and analyze IoT data, it is necessary to integrate them from different parties. However, how to match the same entity’s records in huge amounts of data accurately and ensure data privacy has become an urgent problem to be solved. Therefore, privacy preserving record linkage technology is proposed. However, the existing method often has problems in adaptability and linkage quality. We propose the first privacy preserving record linkage scheme based on deep learning techniques. In our scheme, a combined bloom filter module is proposed to encode the data, which can improve security and protect privacy. At the same time, by introducing the record matching module based on siamese neural network, the adaptability and linkage quality are improved. Experiments show that SNN-PPRL can achieve up to 94% matching accuracy and 99% recall.

DPrio: Efficient Differential Privacy with High Utility for Prio

Private data collection systems such as Prio ensure data privacy by distributing trust among a set of mutually trusted parties, to allow for aggregate data collection without disclosing any single client's data in the clear. While systems like Prio are undergoing widespread interest and adoption, these systems lack efficient mechanisms to provide differential privacy guarantees. In this work, we present a lightweight method that we call DPrio to augment Prio and related systems with differential privacy assurances while ensuring higher data utility than existing noise generation protocols. We compare our results against four related constructions in the literature, and identify how DPrio achieves improved data utility relative to the assumed number of dishonest clients and servers, with only minimal (and batchable) server communication overhead. We present several case studies and discuss considerations for real-world implementations.

HIDE-Healthcare IoT Data Trust ManagEment: Attribute centric intelligent privacy approach

The cloud-based Internet of Things (IoTs) storage enables patients to monitor their health remotely and offers services for physicians of various Medical Institutions (MIs) to diagnose and treat them on time. As a matter of trust, patients are legally expected to hide their real identity and ensure data privacy in the cross-domain of IoT-healthcare, whether it is stored correctly or modified due to external and internal attacks in the cloud. Additionally, physicians treat patients and continuously store duplicated data in cloud storage, which increases the cost of computing. In this context, this paper presents HIDE-Healthcare IoT Data privacy trust management framework, focusing on attributes. Patients’ attributes are used to encrypt and decrypt sensory data between patients and different entities by incorporating the idea of trustworthy and secure shared keys. HIDE uses an intelligent object’s pointer to store the same patient’s sensory data in various versions to prevent data duplication, which will help track MIs that treat patients. An intelligent content-based emergency data access control is developed to monitor multiple patient health criticalities in HIDE. The security analysis and experimental evaluation attest to the benefits of the proposed HIDE framework, considering security and privacy metrics.

Optimal Device Selection in Federated Learning for Resource-Constrained Edge Networks

Low latency, resource efficiency, and data privacy are some of the crucial requirements in modern communication networks. Federated learning can efficiently address these issues by utilizing the data at the network edge and processing massive amounts of data in parallel at the edge devices, thus ensuring data privacy and low latency. For a large-scale federated learning task spanning many devices, challenges arise due to device heterogeneity, data variability, and limited network resources. Optimal selection of edge devices participating in federated learning is essential to attaining resilient, reliable, and resource-efficient edge networks. In this context, this paper proposes an optimal device selection method to minimize redundant data training and improve network resource utilization without affecting the performance of federated learning over resource-constrained edge networks. The proposed optimal device selection method aims to minimize network resource demands while maximizing data diversity within the aggregated model. The performance of the proposed federated learning framework is evaluated using a publicly available image dataset of handwritten digits, EMNIST (an extended version of the MNIST dataset). Experimental results indicate that the proposed framework can obtain accuracy convergence performance on par with conventional federated learning methods while significantly reducing device usage (up to 50%) and resource utilization (up to 30%) while reaching 99% of achievable accuracy. The proposed method can therefore be effectively applied to resource-constrained edge networks.

ResNetFed: Federated Deep Learning Architecture for Privacy-Preserving Pneumonia Detection from COVID-19 Chest Radiographs

Personal health data is subject to privacy regulations, making it challenging to apply centralized data-driven methods in healthcare, where personalized training data is frequently used. Federated Learning (FL) promises to provide a decentralized solution to this problem. In FL, siloed data is used for the model training to ensure data privacy. In this paper, we investigate the viability of the federated approach using the detection of COVID-19 pneumonia as a use case. 1411 individual chest radiographs, sourced from the public data repository COVIDx8 are used. The dataset contains radiographs of 753 normal lung findings and 658 COVID-19 related pneumonias. We partition the data unevenly across five separate data silos in order to reflect a typical FL scenario. For the binary image classification analysis of these radiographs, we propose ResNetFed, a pre-trained ResNet50 model modified for federation so that it supports Differential Privacy. In addition, we provide a customized FL strategy for the model training with COVID-19 radiographs. The experimental results show that ResNetFed clearly outperforms locally trained ResNet50 models. Due to the uneven distribution of the data in the silos, we observe that the locally trained ResNet50 models perform significantly worse than ResNetFed models (mean accuracies of 63% and 82.82%, respectively). In particular, ResNetFed shows excellent model performance in underpopulated data silos, achieving up to +34.9 percentage points higher accuracy compared to local ResNet50 models. Thus, with ResNetFed, we provide a federated solution that can assist the initial COVID-19 screening in medical centers in a privacy-preserving manner.

Data Leakage Detection using Cloud Computing

Abstract: "Data Leakage Detection Using Cloud Computing" is a project that aims to address the growing need to protect sensitive data from unauthorized access and leakage in cloud computing environments. With the increasing use of cloud-based services, there is a growing concern about data privacy and security. The project proposes a solution that leverages cloud computing technology to detect and prevent data leakage in real-time .The proposed system employs data classification, access control, and monitoring mechanisms to prevent unauthorized data access and ensure data privacy. The system is designed to monitor user activities and detect any suspicious behavior that may indicate a potential data leak. The system uses machine learning algorithms to detect anomalies in user behavior and classify data based on its sensitivity level. The system then applies access control policies to restrict user access to sensitive data.

Blockchain-Enabled Vaccination Registration and Verification System in Healthcare Management

Client-server-based healthcare systems are unable to manipulate a high data volume, prone to a single failure point, limited scalability, and data integrity. Particularly, several measures introduced to help curb the spread of Covid-19 were not effective and patient records were not adequately managed and maintained. Most vaccination-proof certificates were forged by unauthorized parties and no standard verification medium exists. Therefore, this paper proposes a blockchain-enabled vaccination management system (VMS). VMS utilizes smart contracts to store encrypted patients record, generate vaccination certificates, and verify the legitimacy of the certificate using a QR code. VMS prototype is implemented using Ethereum, a public blockchain and simulations performed based on Apache JMeter and Hyperledger Caliper to assess its performance in terms of throughput, latency and response time, and the average time per transaction. Results show VMS achieved an average: response time of 132.24 ms, the throughput of 379.89 tps, latency of 204.60 ms, and time of transactions is 10s-12s for 1000 transactions. Also, its comparison with the centralized database shows the traditional database’s effectiveness in transaction processing but lacks data privacy and security strengths. We, therefore, recommend the use of blockchain in the healthcare system and other related sectors such as elections, and student records management to ensure data privacy and security and rid the system of a single point of failure.

Guest Editorial: Blockchain and decentralised solutions for social good

Guest Editorial: Blockchain and decentralised solutions for social good

A Privacy-Preserving Framework Using Homomorphic Encryption for Smart Metering Systems

Smart metering systems (SMSs) have been widely used by industrial users and residential customers for purposes such as real-time tracking, outage notification, quality monitoring, load forecasting, etc. However, the consumption data it generates can violate customers’ privacy through absence detection or behavior recognition. Homomorphic encryption (HE) has emerged as one of the most promising methods to protect data privacy based on its security guarantees and computability over encrypted data. However, SMSs have various application scenarios in practice. Consequently, we used the concept of trust boundaries to help design HE solutions for privacy protection under these different scenarios of SMSs. This paper proposes a privacy-preserving framework as a systematic privacy protection solution for SMSs by implementing HE with trust boundaries for various SMS scenarios. To show the feasibility of the proposed HE framework, we evaluated its performance on two computation metrics, summation and variance, which are often used for billing, usage predictions, and other related tasks. The security parameter set was chosen to provide a security level of 128 bits. In terms of performance, the aforementioned metrics could be computed in 58,235 ms for summation and 127,423 ms for variance, given a sample size of 100 households. These results indicate that the proposed HE framework can protect customer privacy under varying trust boundary scenarios in SMS. The computational overhead is acceptable from a cost–benefit perspective while ensuring data privacy.