Ensure Data Privacy Research Articles

A Hybrid Cryptographic Mechanism for Secure Data Transmission in Edge AI Networks

As Edge AI systems become more prevalent, ensuring data privacy and security in these decentralized networks is essential. In this work, a novel hybrid cryptographic mechanism was presented by combining Ant Lion Optimization (ALO) and Diffie–Hellman-based Twofish cryptography (DHT) for secure data transmission. The developed work collects the data from the created edge AI system and processes it using the Autoencoder. The Autoencoder learns the data patterns and identifies the malicious data entry. The Diffie–Hellman (DH) key exchange generates a shared secret key for encryption, while the ALO optimizes the key exchange and improves security performance. Further, the Twofish algorithm performs the data encryption using a generated secret key, preventing security threats during transmission. The implementation results of the study show that it achieved a higher accuracy of 99.45%, lower time consumption of 2 s, minimum delay of 0.8 s, and reduced energy consumption of 3.2 mJ.

SURVEY ON TECHNICAL ADVANCEMENTS AND RENOVATIONS IN FEDERATED LEARNING

With the rapid increase in IoT devices and advanced machine learning and deep learning techniques, there has been a growing concern about computational cost and data privacy issues since the data coming from IoT devices is non-independent identically distributed (non-IID). However, the implementation of the federated learning algorithm has proven to be a booster in the performance and a solution to the existing data privacy concerns. This paper gives insight into topics such as Blockchain, Unmanned Aerial Vehicles (UAV), Wireless communication, Vehicular Internet of Things, Healthcare, and Cloud Computing and how they have been implemented and co-related to federated Learning and the application and the emerging use cases in the field of federated learning (FL) with respect to the above-mentioned topics have also been discussed. This paper uniquely shows how federated learning has an edge over the traditional machine learning and deep learning techniques in IoT infrastructure since computing nodes are trained using local models on the devices and then these local models are uploaded to the central global server instead of data directly into a global model on a central server ensuring data privacy.

Review of smart water management: IoT and AI in water and wastewater treatment

Integrating the Internet of Things (IoT) and Artificial Intelligence (AI) in smart water management revolutionizes sustainable water resource utilization. This comprehensive review explores these technologies' benefits, challenges, regulatory implications, and future trends. Smart water management enhances operational efficiency, predictive maintenance, and resource conservation while addressing data security and infrastructure investment challenges. Regulatory frameworks play a pivotal role in shaping the responsible deployment of AI and IoT, ensuring data privacy and ethical use. Future trends include advanced sensors, decentralized systems, quantum computing, and blockchain for enhanced water data security. The alignment with Sustainable Development Goals (SDGs) underscores the transformative potential of smart water management in achieving universal access to clean water, climate resilience, and inclusive, sustainable development. As we embrace these technologies, collaboration, public awareness, and ethical considerations will guide the evolution of intelligent and equitable water management systems.

Application Of K-Means Clustering For Customer Segmentation In Grocery Stores In Kenya

The retail industry, particularly in the context of grocery stores, plays a vital role in meeting consumers' daily needs. To optimize marketing strategies and enhance customer satisfaction, understanding customer behavior and preferences is crucial. Customer segmentation, a powerful market research technique, enables businesses to group customers with shared characteristics into distinct segments, allowing targeted and personalized approaches. This article explores the application of the K-means clustering algorithm for customer segmentation in grocery stores within the unique context of Kenya. By leveraging transactional and demographic data from diverse grocery stores across Kenya, the study aims to identify homogeneous customer groups with similar purchasing behaviors and preferences. The data collection process involved obtaining consent from store owners and ensuring data privacy and security. Following data preprocessing, K-means clustering was applied, and various validation techniques were utilized to determine the optimal number of clusters. The results yielded valuable insights into customer segments, aiding the identification of key customer groups and their distinct preferences.

A smart decentralized identifiable distributed ledger technology‐based blockchain (DIDLT‐BC) model for cloud‐IoT security

AbstractThe most important and difficult challenge the digital society has recently faced is ensuring data privacy and security in cloud‐based Internet of Things (IoT) technologies. As a result, many researchers believe that the blockchain's Distributed Ledger Technology (DLT) is a good choice for various clever applications. Nevertheless, it encountered constraints and difficulties with elevated computing expenses, temporal demands, operational intricacy, and diminished security. Therefore, the proposed work aims to develop a Decentralized Identifiable Distributed Ledger Technology‐Blockchain (DIDLT‐BC) framework that is intelligent and effective, requiring the least amount of computing complexity to ensure cloud IoT system safety. In this case, the Rabin algorithm produces the digital signature needed to start the transaction. The public and private keys are then created to verify the transactions. The block is then built using the DIDLT model, which includes the block header information, hash code, timestamp, nonce message, and transaction list. The primary purpose of the Blockchain Consent Algorithm (BCA) is to find solutions for numerous unreliable nodes with varying hash values. The novel contribution of this work is to incorporate the operations of Rabin digital data signature generation, DIDLT‐based blockchain construction, and BCA algorithms for ensuring overall data security in IoT networks. With proper digital signature generation, key generation, blockchain construction and validation operations, secured data storage and retrieval are enabled in the cloud‐IoT systems. By using this integrated DIDLT‐BCA model, the security performance of the proposed system is greatly improved with 98% security, less execution time of up to 150 ms, and reduced mining time of up to 0.98 s.

Knowledge, Attitude and Ethical Perception Towards Precision Medicine Among Healthcare Professionals- a Questionnaire Survey

Precision medicine, also known as personalized medicine, is an emerging approach in healthcare that tailors medical treatment and prevention strategies to individual patients based on their unique genetic makeup, lifestyle, and environmental factors. It represents a paradigm shift from the traditional “one size fits all” approach to a more targeted and precise form of medical care. In precision medicine, extensive genomic analysis and other high-throughput technologies are employed to identify genetic variations and molecular markers associated with specific diseases or treatment responses. This wealth of genetic information allows healthcare professionals to make more accurate diagnoses, predict disease risks, and design personalized treatment plans. The application of precision medicine spans various medical fields, including oncology, cardiovascular diseases, neurology, and infectious diseases. In oncology, for example, molecular profiling of tumors enables the identification of specific genetic alterations that guide the selection of targeted therapies, leading to improved patient outcomes and reduced adverse effects. Precision medicine also holds great promise in preventive medicine. By identifying an individual’s genetic predispositions to certain diseases, clinicians can recommend tailored lifestyle modifications or interventions to mitigate the risk. Additionally, the integration of electronic health records, wearable devices, and mobile health applications facilitates real-time monitoring and personalized interventions, enhancing patient engagement and overall healthcare outcomes. Despite its potential, several challenges need to be addressed for the widespread implementation of precision medicine. These include the high cost of genomic sequencing and data analysis, ensuring data privacy and security, and the need for interdisciplinary collaboration among researchers, clinicians, and policymakers. In conclusion, precision medicine represents a transformative approach to healthcare, enabling personalized and targeted interventions that have the potential to revolutionize patient care, improve treatment outcomes, and reduce healthcare costs. Efforts to overcome the challenges associated with precision medicine are crucial for its successful integration into routine clinical practice.

A Network Intrusion Detection Method for Various Information Systems Based on Federated and Deep Learning

Under the premise of ensuring data privacy, traditional network intrusion detection (NID) methods cannot achieve high accuracy for different types of intrusions. A NID method combining transformer and federated learning (FedL) is proposed for this purpose. First, a multi-party collaborative learning framework was built based on FedL, which achieved data exchange and sharing. Then, by introducing the self-attention mechanism (AttM) to improve the traditional transformer, it could quickly converge. Finally, an NID model integrating transformer and FedL was constructed by combining DNN, GRU, and an encoder module composed of improved transformer, achieving accurate detection of network intrusion. The proposed NID method was compared with the other three methods. The results show that the proposed method has the highest NID accuracy and F1 score on the NSL-KDD and UNSW-NB15 dataset, with the highest accuracy reaching 99.65% and 89.25%, while the F1 score has the highest accuracy, reaching 99.45% and 88.13%, outperforming the other three comparative algorithms in terms of performance.

Blockchain-based engine data trustworthy swarm learning management method

Engine data management is of great significance for ensuring data security and sharing, as well as facilitating multi-party collaborative learning. Traditional data management approaches often involve decentralized data storage that is vulnerable to tampering, making it challenging to conduct multi-party collaborative learning under privacy protection conditions and fully leverage the value of data. Moreover, data with compromised integrity can lead to incorrect results if used for model training. Therefore, this paper aims to break down data sharing barriers and fully utilize decentralized data for multi-party collaborative learning under privacy protection conditions. We propose a trustworthy engine data management method based on blockchain technology to ensure data immutability and non-repudiation. To address the issue of limited data samples for some users resulting in poor model performance, we introduce swarm learning techniques based on centralized machine learning and design a trustworthy data management method for swarm learning, achieving trustworthy regulation of the entire process. We conduct research on engine models under swarm learning based on the NASA open dataset, effectively organizing decentralized data samples for collaborative training while ensuring data privacy and fully leveraging the value of data.

Digital health in perinatal care: Exploring usage, attitudes, and needs among Swiss women in urban and rural settings

Background Switzerland's healthcare system is known for its quality but faces challenges such as slow digitalization and fragmentation, especially in perinatal care. This study investigates Swiss women's use, needs, and attitudes in respect of digital health tools during pregnancy and postpartum, focusing on any differences between rural and urban populations. Methods A web-based cross-sectional survey targeted pregnant women and those who had given birth in the last 12 months. Participants were recruited through social media, and the data were analyzed using principal component analysis and multivariable regressions to explore factors affecting the use of digital tools and attitudes toward eHealth. Results A total of 1160 participants completed the survey. Healthcare professionals (92%) and private networks (77%) were the primary sources of information. Women expressed a strong preference for app features such as data access (73%), prescription management (73%), and scheduling appointments with healthcare professionals (71%). However, they also raised concerns about the impersonal nature of digital healthcare interactions (71%). Overall, rural women had more negative attitudes toward online health information seeking, which can be attributed to differences in education levels. Conclusion The findings indicate that while Swiss women in the perinatal period do utilize digital tools, they focus more on nonmedical topics such as tracking physiological development. The study underscores the importance of adapting digital health solutions to the specific needs of women in the perinatal period. Emphasis should be placed on developing applications that are not only informative but also empower women on their healthcare journey while ensuring data privacy and supporting personal interactions with healthcare providers.

Confidential Transaction Balance Verification by the Net Using Non-Interactive Zero-Knowledge Proofs

One of the main trends for the monitoring and control of business processes is to implement these processes via private blockchain systems. These systems must ensure data privacy and verifiability for the entire network here denoted by ‘Net’. In addition, every business activity should be declared to a trusted third party (TTP), such as an Audit Authority (AA), for tax declaration and collection purposes. We present a solution for a confidential and verifiable realization of transactions based on the Unspent Transaction Output (UTxO) paradigm. This means that the total sum of transaction inputs (incomes) $In$ must be equal to the total sum of transaction outputs (expenses) $Ex$, satisfying the balance equation $In=Ex$. Privacy in a private blockchain must be achieved through the encryption of actual transaction values. However, it is crucial that all participants in the network be able to verify the validity of the transaction balance equation. This poses a challenge with probabilistically encrypted data. Moreover, the inputs and outputs are encrypted with different public keys. With the introduction of the AA, the number of different public keys for encryption can be reduced to two. Incomes are encrypted with the Receiver’s public key and expenses with the AA’s public key. The novelty of our realization lies in taking additively-multiplicative, homomorphic ElGamal encryption and integrating it with a proposed paradigm of modified Schnorr identification providing a non-interactive zero-knowledge proof (NIZKP) using a cryptographically secure h-function. Introducing the AA as a structural element in a blockchain system based on the UTxO enables effective verification of encrypted transaction data for the Net. This is possible because the proposed NIZKP is able to prove the equivalency of two ciphertexts encrypted with two different public keys and different actors. This integration allows all users on the Net to check the UTxO-based transaction balance equation on encrypted data. The security considerations of the proposed solution are presented.

Leveraging ChatGPT for Effective Research Design and Data Analysis — An Example with Research on Short Video Addiction

Artificial intelligence, particularly advanced models like ChatGPT, offers significant enhancements in data processing efficiency, decision-making velocity, and precision across various domains. This study explores the transformative potential of ChatGPT within mixed methods research, emphasizing its integration into the exploratory sequential design against a backdrop of increasing reliance on AI in academic settings. Focusing on short video addiction as a research topic, this research employed ChatGPT to develop a qualitative research framework and refine interview outlines before utilizing it to design quantitative research instruments informed by qualitative insights. Results indicate that ChatGPT not only streamlined data collection, analysis, and interpretation but also emphasized the need for human oversight to interpret AI outputs and ensure data privacy. Conclusively, while ChatGPT significantly enhances the efficiency and depth of research methodologies, it also highlights the challenges of maintaining ethical standards and methodological integrity. Continuous refinement and responsible integration of AI tools like ChatGPT are essential to advance methodological sophistication and ensure the reliability of research findings.

ILLUMINATING AUTONOMY FEDERATED LEARNING FOR OBJECT DETECTION IN AUTONOMOUS VEHICLES UNDER LOW LIGHT

Autonomous vehicles rely heavily on object detection systems to navigate safely, yet low-light conditions pose significant challenges to these systems. Traditional centralized machine learning approaches require substantial data aggregation, raising privacy and security concerns. This paper introduces an innovative approach, Illuminating Autonomy Federated Learning (IAFL), designed to enhance object detection in autonomous vehicles operating under low-light conditions. IAFL leverages federated learning to train object detection models across multiple vehicles without centralizing the data, thus preserving privacy while utilizing diverse realworld driving data. The approach integrates advanced image enhancement techniques to improve low-light object visibility, combined with robust federated learning algorithms to ensure model accuracy and generalization across varying environments. Extensive simulations and real-world testing demonstrate that IAFL significantly improves detection performance in low-light scenarios, maintaining high accuracy and efficiency while ensuring data privacy and security. This research marks a pivotal step toward safer and more reliable autonomous driving in challenging lighting conditions.

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals.

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the previous active learning (e.g., AADA and MHPL) and UDA (e.g., Tent and CPR) methods, and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research (Git).

Assessing the Impact of Defacing Algorithms on Brain Volumetry Accuracy in MRI Analyses.

To ensure data privacy, the development of defacing processes, which anonymize brain images by obscuring facial features, is crucial. However, the impact of these defacing methods on brain imaging analysis poses significant concern. This study aimed to evaluate the reliability of three different defacing methods in automated brain volumetry. Magnetic resonance imaging with three-dimensional T1 sequences was performed on ten patients diagnosed with subjective cognitive decline. Defacing was executed using mri_deface, BioImage Suite Web-based defacing, and Defacer. Brain volumes were measured employing the QBraVo program and FreeSurfer, assessing intraclass correlation coefficient (ICC) and the mean differences in brain volume measurements between the original and defaced images. The mean age of the patients was 71.10±6.17 years, with 4 (40.0%) being male. The total intracranial volume, total brain volume, and ventricle volume exhibited high ICCs across the three defacing methods and 2 volumetry analyses. All regional brain volumes showed high ICCs with all three defacing methods. Despite variations among some brain regions, no significant mean differences in regional brain volume were observed between the original and defaced images across all regions. The three defacing algorithms evaluated did not significantly affect the results of image analysis for the entire brain or specific cerebral regions. These findings suggest that these algorithms can serve as robust methods for defacing in neuroimaging analysis, thereby supporting data anonymization without compromising the integrity of brain volume measurements.

Integrating Improved Mobile Net and Homomorphic Encryption in Hybrid IoT Security Frameworks for Enhanced Resilience Against Advanced Persistent Threats

IoT devices have transformed smart cities and healthcare. The expanding usage of IoT devices creates major security threats, leaving critical systems vulnerable to sophisticated and persistent assaults. Our hybrid IoT security approach employs homomorphic encryption and improved MobileNet to protect data and simplify feature extraction. Our extensive testing and assessment prove that the proposed structure makes IoT settings more resistant to sophisticated persistent attacks. We discovered superior methodologies for F1 score, accuracy, precision, and memory performance measurement. To ensure data privacy and security during analysis and transmission, homomorphic encryption is incorporated. Our ablation research lays out each framework component's contributions. To increase system speed, it emphasizes safe data processing, real-time analytical optimization, lightweight feature extraction, and privacy-preserving computing. The scalability study indicates that the framework can scale with IoT installations while maintaining peak performance and resource efficiency. Finally, the hybrid IoT security architecture improves IoT security. It provides a full and effective security solution for IoT infrastructure. Lawmakers, business experts, and students in the sector may learn from this research regarding genuine IoT security systems.

Pioneering Security: A Hybrid Logic Framework for the Future of IoT Protection

In a complicated situation, the Internet of Things simplifies the process of connecting a wide range of things. Because of its openness and lack of human control, the Internet of Things is open to assaults like denial of service (DoS) and man-in-the-middle attacks. Furthermore, any device that can connect to the internet may be hacked. These attacks put the network connections and the actual equipment at danger. IoT security and privacy will so be compromised. Due to its limited power, bandwidth, and storage, the Internet of Things requires a security solution that does not overload it. This study aims to preserve consumers' trust in the Internet of Things (IoT) by safeguarding their data against unauthorised access and maintaining the privacy of their personal information. With the ultimate objective of presenting a unique hybrid and optimised lightweight logical security architecture to ensure data privacy and integrity while making effective use of available resources, all research are carried out with this purpose in mind. The Hybrid Lightweight Security Framework (HLSF), which this study suggests, offers secrecy and integrity in addition to authentication. The structure consists of three distinct steps. The first step is registration, the second is authentication, and the third is transit security, which protects data while it is being transported. Compared to other current frameworks, the results reveal that HLSF performs better in terms of precision, recall, and accuracy when applied to an IoT situation.

Використання блокчейну першого рівня LTO Network для автоматизації міжорганізаційних бізнес-процесів

The article is devoted to the consideration of blockchain technology as a means of automating inter-organizational business processes. It is noted that the digitalization and automation of internal business processes bring significant benefits in terms of increasing productivity and reducing costs. However, organizations face difficulties when trying to obtain such benefits for inter-organizational processes, partly due to a lack of trust. This paper considers a blockchain approach to address this problem. Bitcoin has demonstrated how blockchain can provide a distributed system without the need for trust through cryptography and decentralization. LTO extends this approach with a decentralized workflow engine for ad-hoc collaboration. Information is exchanged between participants through private event chains for each process and hashed on a public blockchain. This hybrid approach allows organizations to comply with data protection requirements and avoid scalability issues typical of blockchain projects. The advantages of LTO live contracts over Ethereum smart contracts are discussed. Live contracts do not contain values directly, but describe the interaction between the parties, unlike the cryptographic "boxes" of smart contracts. Modeling live contracts as finite state machines allows visualizing them in the form of a flowchart. Alternative modeling techniques such as Petri nets and BPMN are discussed. The article describes a scenario-based approach to defining a workflow containing states, actions, participants, assets, and embedded data objects. The features of data objects, participant identifiers, process execution and its adaptability are detailed. The author explains the operation of a private event chain as an ad-hoc private blockchain that ensures the consistency of the process state between nodes. Methods of ensuring data privacy are discussed. The second part of the article is devoted to the global public blockchain LTO to confirm information from private event chains. The consensus algorithm is explained based on the importance of token ownership and its use to prevent abuse. Optimizations, such as the NG protocol and aggregation blocks, are discussed to scale with a large number of transactions. The last part describes the architecture of the platform and its components: microservices, application layers, container orchestration. The platform makes it easy to digitalize and automate inter-organizational interactions using live contracts on the blockchain.

Artificial intelligence in dentistry: It’s applications, impact and challenges

In dentistry, artificial intelligence (AI) has shown great potential in improving diagnosis, treatment planning, and patient care. AI algorithms have been developed to analyze dental images, such as radiographs and intraoral scans, aiding in the detection of dental caries, periodontal diseases, and oral lesions. These algorithms can provide more accurate and efficient diagnoses, reducing the reliance on human interpretation. AI has also been utilized in treatment planning and helping dentists to determine the optimal approach for procedures such as dental implant placement and orthodontic treatment, and recently, AI has also been playing a significant role in forensic medicine as well as in forensic odontology. In addition, AI-based chatbots and virtual assistants have been developed to provide patients with personalized oral health information and guidance. Despite the numerous advantages, challenges remain in implementing AI in dentistry, such as ensuring data privacy and addressing ethical concerns. Nevertheless, AI has the potential to revolutionize dentistry by improving diagnosis, treatment planning, and patient care, ultimately leading to better oral health outcomes. This review provides an overview of the current applications of AI and its influence on dental practice, along with future prospects.

Data Sharing Scheme of Smart Grid Based on Identity Condition Proxy Re-Encryption

A large amount of high-value data are stored in smart grid data centers, but since the resource diversity of a single data center is limited, data sharing becomes especially important in conducting an effective data mining process. However, traditional data-sharing models often use centralized schemes without authentication of the shared objects, making it difficult to establish trust relationships and ensure data privacy. This makes it difficult to break through the problem of data islands. To solve the above problems, this paper proposes a blockchain-based data-sharing incentive model for edge smart grid scenarios. First, the model uses blockchain and proxy re-encryption technology to achieve the security and traceability of the smart grid data-sharing process. Secondly, the data-sharing incentive algorithm is designed using game theory to maximize the willingness of data owners to share data. Finally, the model in this paper is compared and analyzed with other existing data-sharing models, and the designed performance test shows that the scheme in this paper has significant advantages over the other literature schemes in terms of functionality and computational overhead, and the increase in costs is not significant, and the model can meet the requirements for large-scale data sharing in edge smart grid scenarios.

Federated Distillation Methodology for Label-Based Group Structures

In federated learning (FL), clients train models locally without sharing raw data, ensuring data privacy. In particular, federated distillation transfers knowledge to clients regardless of the model architecture. However, when groups of clients with different label distributions exist, sharing the same knowledge among all clients becomes impractical. To address this issue, this paper presents an approach that clusters clients based on the output of a client model trained using their own data. The clients are clustered based on the predictions of their models for each label on a public dataset. Evaluations on MNIST and CIFAR show that our method effectively finds group identities, increasing accuracy by up to 75% over existing methods when the distribution of labels differs significantly between groups. In addition, we observed significant performance improvements on smaller client groups, bringing us closer to fair FL.