Ensure Data Privacy Research Articles

Athlete Perspectives on AI-Driven Coaching Technologies: A Qualitative Inquiry

This article seeks to understand athletes' perceptions and experiences with AI-driven coaching technologies. The objective is to identify the major themes associated with the adoption, perceived benefits, challenges, and impact of these technologies on motivation and performance, alongside suggestions for future enhancements. Eighteen athletes across various sports disciplines participated in semi-structured interviews. The study employed a thematic analysis approach to identify key themes and sub-themes related to athletes' experiences with AI-driven coaching technologies. Ethical approval was obtained from the Institutional Review Board, and informed consent was secured from all participants. Five major themes were identified: Adoption and Adaptation, Perceived Benefits, Challenges and Limitations, Impact on Motivation and Performance, and Suggestions for Improvement. Athletes expressed initial skepticism followed by appreciation for the personalized training and efficiency offered by AI technologies. However, concerns regarding technical issues, lack of personal touch, and data privacy were noted. The impact on motivation and performance was predominantly positive, with athletes acknowledging the role of AI in enhancing training outcomes. Suggestions for improvement emphasized the need for more intuitive user interfaces, enhanced data security, and better integration of human coaching elements. Athletes recognize the transformative potential of AI-driven coaching technologies in enhancing training efficiency, personalization, and injury prevention. Nonetheless, the successful adoption of these technologies hinges on addressing technical challenges, ensuring data privacy, and maintaining the human element in coaching. Future developments in AI-driven coaching technologies should consider athletes' feedback to refine and tailor these tools, ensuring they meet the unique needs of individuals and foster a productive coach-athlete relationship. Suggestions for future research include expanding the participant pool across a wider range of sports and incorporating longitudinal studies to explore the evolution of athlete perspectives over time.

Federated Learning Based on Data Divergence and Differential Privacy in燜inancial Risk Control Research

In the financial sector, data are highly confidential and sensitive, and ensuring data privacy is critical. Sample fusion is the basis of horizontal federation learning, but it is suitable only for scenarios where customers have the same format but different targets, namely for scenarios with strong feature overlapping and weak user overlapping. To solve this limitation, this paper proposes a federated learning-based model with local data sharing and differential privacy. The indexing mechanism of differential privacy is used to obtain different degrees of privacy budgets, which are applied to the gradient according to the contribution degree to ensure privacy without affecting accuracy. In addition, data sharing is performed to improve the utility of the global model. Further, the distributed prediction model is used to predict customers’ loan propensity on the premise of protecting user privacy. Using an aggregation mechanism based on federated learning can help to train the model on distributed data without exposing local data. The proposed method is verified by experiments, and experimental results show that for non-iid data, the proposed method can effectively improve data accuracy and reduce the impact of sample tilt. The proposed method can be extended to edge computing, blockchain, and the Industrial Internet of Things (IIoT) fields. The theoretical analysis and experimental results show that the proposed method can ensure the privacy and accuracy of the federated learning process and can also improve the model utility for non-iid data by 7% compared to the federated averaging method (FedAvg).

Privacy amplification for wireless federated learning with Rényi differential privacy and subsampling

<abstract><p>A key issue in current federated learning research is how to improve the performance of federated learning algorithms by reducing communication overhead and computing costs while ensuring data privacy. This paper proposed an efficient wireless transmission scheme termed the subsampling privacy-enabled RDP wireless transmission system (SS-RDP-WTS), which can reduce the communication and computing overhead in the process of learning but also enhance the privacy protection ability of federated learning. We proved our scheme's convergence and analyzed its privacy guarantee, as well as demoonstrated the performance of our scheme on the Modified National Institute of Standards and Technology database (MNIST) and Canadian Institute for Advanced Research, 10 classes datasets (CIFAR10).</p></abstract>

Delegated Computation Architecture and Implementation for Numerical Simulation of Electromagnetic Comprehensive Performance Under the Framework of Blockchain

In view of the significant computational resources required for numerical simulation of electromagnetic comprehensive performance in modern electrical equipment, and the security and transaction fairness issues of existing delegated computation schemes, we propose a delegated computation scheme under the blockchain framework that is suitable for numerical simulation of electromagnetic comprehensive performance. This scheme combines the multi-level data characteristics of electromagnetic field numerical simulations with practical needs. Firstly, we propose a method for handling multi-layered data in delegated computation. Secondly, we design a delegated computation architecture that involves at least three participants and a detailed transaction mechanism, and develop three smart contracts to ensure data privacy protection and transaction fairness in delegated computation. Finally, the feasibility of the proposed scheme is proved by delegated computations for “Solving the Poisson equation of two-dimensional electrostatic field” and “Optimization of amorphous alloy transformer”. The research results have achieved a reliable and credible solution to the large-scale data computing problem in the field of numerical simulation of electromagnetic comprehensive performance, and provided a new theory and solution for data security in this field.

FEDERATED LEARNING FOR VISION-BASED OBSTACLE AVOIDANCE IN MOBILE ROBOTS

Federated learning (FL) is a machine learning approach that allows multiple devices or systems to train a model collaboratively, without exchanging their data. This is particularly useful for autonomous mobile robots, as it allows them to train models customized to their specific environment and tasks, while keeping the data they collect private. Research Objective to train a model to recognize and classify different types of objects, or to navigate around obstacles in its environment. Materials and me¬thods we used FL to train models for a variety of tasks, such as object recognition, obstacle avoidance, localization, and path planning by an autonomous mobile robot operating in a warehouse FL. We equipped the robot with sensors and a processor to collect data and perform machine learning tasks. The robot must communicate with a central server or cloud platform that coordinates the training process and collects model updates from different devices. We trained a neural network (CNN) and used a PID algorithm to generate a control signal that adjusts the position or other variable of the system based on the difference between the desired and actual values, using the relative, integrative and derivative terms to achieve the desired performance. Results through careful design and execution, there are several challenges to implementing FL in autonomous mobile robots, including the need to ensure data privacy and security, and the need to manage communications and the computational resources needed to train the model. Conclusion. We conclude that FL enables autonomous mobile robots to continuously improve their performance and adapt to changing environments and potentially improve the performance of vision-based obstacle avoidance strategies and enable them to learn and adapt more quickly and effectively, leading to more robust and autonomous systems.

A brief analysis of ChatGPT：historical evolution， current applications，and future prospects

近年来，人工智能技术接连取得突破，尤其是在强化学习、大规模语言模型和人工智能生成内容技术等方面，正逐步成为各个行业的创新驱动力。OpenAI于2022年11月30日发布的ChatGPT由于具有惊人的自然语言理解和生成能力，引起全社会大范围的关注，成为全球热议的话题，并被广泛应用于各个行业。仅两个月后，ChatGPT的月活跃用户数便达1亿，成为史上用户数增长最快的消费者应用。鉴于ChatGPT目前造成的影响，对其进行全面的分析较为必要。本文从历史沿革、应用现状和前景展望这3个角度对ChatGPT进行剖析，探究其对社会的影响、技术的原理和挑战以及未来发展的可能性，并从模型能力的角度简要介绍GPT-4相对于ChatGPT的改进。作为一个现象级技术产品，从技术角度而言ChatGPT对相关领域具有里程碑式的重要意义，从应用角度而言其可能会给人类社会带来巨大的影响。ChatGPT有潜力成为计算机领域最伟大的成就之一。但就目前而言，ChatGPT仍然存在一些局限，尚未达到强人工智能的水平。在当前阶段，研究人员需要对人工智能技术持有自信和谦虚学习的态度，继续发展相关的技术研究和应用。;Artificial intelligence（AI）technology has been developing intensively，especially for such scenarios in relevance to its applications of 1）natural language processing，2）computer vision，3）recommendation systems，and 4）forecast analysis. AI technology has been challenging for human cognition over the past decade. In recent years，natural language processing techniques can be focused on more. ChatGPT，as a case of emerging generative AI technology，is launched in December of 2022. ChatGPT，as an advanced language model，is commonly used on the basis of its a）larger model sizes，b）advanced pre-training methods，c）faster computing resources，and d）more language processing tasks. This ChatGPT-related literature review is focused on its（1）public awareness and application status，（2）characteristics， （3）mechanisms，（4）scalability，（5）challenges and limitations，（6）future development and application prospects，and （7）improvements of GPT-4 relative to ChatGPT. Cognitive computing and AI-based ChatGPT can be as a sort of language model in terms of the Transformer architecture and Generative Pre-Training（GPT）. This GPT-trained model can be related to natural language processing，which can predict the probability distribution of the next token using a multi-layer Transformer to generate natural language text. It can be outreached by training the learned language patterns on a large corpus of text. The OpenAI’s language model has shown a significant improvement in their level of intelligence from GPT-1（117 million parameters）in 2018 to GPT-3（175 billion parameters）in 2020. The language processing and generation capabilities of GPT have been improving dramatically in terms of consistent optimization like its 1）model size，2）generative models， and 3）self-supervised learning. Thereafter，reinforcement learning-based InstructGPT is originated from Human Feedback and such probability of infeasible，untrue，and biased outputs can be significantly reduced in January 2022. In December 2022，ChatGPT is introduced as the sister model of InstructGPT. ChatGPT is not only add InstructGPT-based chat attributes，and a test version is opened to the public. The core technologies of ChatGPT can be linked to 1）reinforcement learning from human feedback（RLHF），2）supervised fine-tuning（SFT），3）instruction fine-tnning（IFT），and 4）chain-ofthought （CoT）as well. ChatGPT has attracked about 100 million active users per month after the launch of two months. In comparison，TikTok took nine months to achieve 100 million monthly active users，and Instagram took two and a half years. According to Similar Web，more than 13 million independent visitors use ChatGPT on average each day in January of 2023，which is more than twice in December of 2022. The leading US new media company Buzzfeed accurately seized the opportunity of ChatGPT and saw its stock price triple in two days. The ChatGPT-derived impact shows its potential preference for consumers. The ChatGPT can play mulitiple roles for such domain like clinics，translation，official administrations，and programming tasks. Such extensive application of ChatGPT is still to be developed. However，while ChatGPT has the potential for widespread application in various industries，it cannot be universally applied to all industries. For example，as certain industrial production processes typically rely on digitalization and do not necessitate the handling of human language，natural language processing techniques may not be required. Furthermore，various other factors，such as legal restrictions and data privacy concerns，may also impinge upon the application of natural language processing technologies within certain industries. For industries that require the processing of sensitive information，such as the healthcare industry，natural language processing technologies may need to comply with strict legal regulations to ensure data privacy and security. In addition to industry-specific reasons，it should be noted that ChatGPT has not yet achieved perfection in natural language processing tasks. In summary，as a phenomenal and technological product，AI-generated ChatGPT’s potentials are beneficial for textual and multi-modal AIGC applications to a certain extent，and it may have an impact on the a）survival of corporations，b）competition among countries，and c）entire social structure. However，the current various positive evaluations of ChatGPT can only be seen as a phenomenon of good rain after a long drought，and it cannot change the fact that ChatGPT is a questions and answers（Q&A）solution based on prior knowledge and models. It is required to be acknowledged that ChatGPT does not have its true recognition，intention，and creativity yet，and its true intelligence need to be tackled further.

A novel privacy protection scheme for internet of things based on blockchain and privacy set intersection technique

In the era of big data, an ocean of data generated by Internet of Things (IoT) devices will be analyzed and processed by cloud computing. However, outsourcing of data can lead to leakage of user privacy to those unreliable service providers. In this paper, we propose a novel privacy-preserving scheme for IoT device by employing privacy set intersection (PSI) and blockchain technique to achieve data privacy. First, a homomorphic encryption PSI technique based on 0-1 encoding is proposed, which well hides the set base to ensure data privacy. Second, combining blockchain structure and smart contract, the proposed scheme can improve the efficiency of data sharing by storing the shared data on a blockchain. Third, the security analysis shows that the scheme has extremely high control over the individual data and can ensure the security and privacy of the data. Finally, we compare the functionality with other relevant schemes and demonstrate that our scheme functions well with low communication and computational overhead.

InFEDge: A Blockchain-Based Incentive Mechanism in Hierarchical Federated Learning for End-Edge-Cloud Communications

Advances in communications and networking technologies are driving the computing paradigm toward the end-edge-cloud collaborative architecture to leverage ubiquitous data and resources. Opposite to centralized intelligence, Hierarchical Federated Learning (HFL) relieves overwhelmed communication overhead and enjoys the advantages of high bandwidth as well as abundant computing resources while retaining privacy-preserving benefits of Federated Learning (FL). It is difficult to balance system overhead and model performance in the HFL framework, while it could be solved by introducing an incentive mechanism. Although the incentive mechanism can alleviate the above anxiety by compensating relevant participants, some limitations (multi-dimensional properties, incomplete information and unreliable participants) will significantly degrade the performance and efficiency of the designed mechanism. To address the challenges caused by the above limitations, we propose InFEDge, a blockchain-based incentive mechanism in the HFL. The InFEDge considers 1) multi-dimensional individual properties to model system participants and proves the uniqueness of Nash equilibrium with the closed-form solution. Meanwhile, 2) we transform the problem under incomplete information into a contract game where we obtain the optimal solution. Moreover, 3) we also leverage the blockchain to provide economic incentives, prevent unreliable participants’ disturbance and further ensure data privacy by implementing the mechanism in the smart contract to offer a credible, faster, and transparent resource trading system. Experimental evaluations on a proof-of-concept testbed along with real traces demonstrate the superiority of our mechanism. Further, our method solves a real-world user allocation problem for future communications and networking.

DyCo: Dynamic, Contextualized AI Models

Devices with limited computing resources use smaller AI models to achieve low-latency inferencing. However, model accuracy is typically much lower than the accuracy of a bigger model that is trained and deployed in places where the computing resources are relatively abundant. We describe DyCo, a novel system that ensures privacy of stream data and dynamically improves the accuracy of small models used in devices. Unlike knowledge distillation or federated learning, DyCo treats AI models as black boxes. DyCo uses a semi-supervised approach to leverage existing training frameworks and network model architectures to periodically train contextualized, smaller models for resource-constrained devices. DyCo uses a bigger, highly accurate model in the edge-cloud to auto-label data received from each sensor stream. Training in the edge-cloud (as opposed to the public cloud) ensures data privacy, and bespoke models for thousands of live data streams can be designed in parallel by using multiple edge-clouds. DyCo uses the auto-labeled data to periodically re-train, stream-specific, bespoke small models. To reduce the periodic training costs, DyCo uses different policies that are based on stride, accuracy, and confidence information. We evaluate our system, and the contextualized models, by using two object detection models for vehicles and people, and two datasets (a public benchmark and another real-world proprietary dataset). Our results show that DyCo increases the mAP accuracy measure of small models by an average of 16.3% (and up to 20%) for the public benchmark and an average of 19.0% (and up to 64.9%) for the real-world dataset. DyCo also decreases the training costs for contextualized models by more than an order of magnitude.

Masking primal and dual models for data privacy in network revenue management

We study a collaborative revenue management problem where multiple decentralized parties agree to share some of their capacities. This collaboration is performed by constructing a large mathematical programming model that is available to all parties. The parties then use the solution of this model in their own capacity control systems. In this setting, however, the major concern for the parties is the privacy of their input data, along with their individual optimal solutions. We first reformulate a general linear programming model that can be used for a wide range of network revenue management problems. Then we address the data privacy concern of the reformulated model and propose an approach based on solving an equivalent data-private model constructed with input masking via random transformations. Our main result shows that, after solving the data-private model, each party can safely access only its own optimal capacity allocation decisions. We also discuss the security of the transformed problem in the considered multi-party setting. Simulation experiments are conducted to support our results and evaluate the computational efficiency of the proposed data-private model. Our work provides an analytical approach and insights on how to manage shared resources in a network problem while ensuring data privacy. Constructing and solving a collaborative network problem requires information exchange between parties that may not be possible in practice. Including data privacy in decentralized collaborative network revenue management problems with capacity sharing is new to the literature and relevant to practice.

Flexible access control mechanism for cloud stored EHR using consortium blockchain

The implementation of cloud computing to store and access electronic health records has shown substantial benefits for both clinical organizations and patients in managing electronic health records. The prime security issue of cloud-based electronic health records is that the patient is physically unable to own a medical record whereas a clinical organization can maintain one for them. The latter may collude with centralized cloud servers. So, there is a vulnerability of such records being tampered with in order to hide the medical malpractices. So, maintaining data integrity and data privacy becomes a significant challenge when deploying cloud computing. Therefore, in this paper, a consortium blockchain-based cloud-stored electronic health record is proposed which provides data integrity, data privacy, storage scalability, and fine-grained access control. Each process in outsourcing electronic health records to the cloud is incorporated as a transaction in a consortium ethereum blockchain through smart contracts. Through smart contracts, an attribute-based contract key is generated for the users that can decrypt the encrypted data stored in the cloud. The attribute-based contract key allows only users who are authorized to access the information ensuring data privacy and fine-grained access control. Moreover, the proposed scheme is proved to provide tamper-proof although the medical records are controlled by a group of clinical organizations.

Federated Learning in Ocular Imaging: Current Progress and Future Direction.

Advances in artificial intelligence deep learning (DL) have made tremendous impacts on the field of ocular imaging over the last few years. Specifically, DL has been utilised to detect and classify various ocular diseases on retinal photographs, optical coherence tomography (OCT) images, and OCT-angiography images. In order to achieve good robustness and generalisability of model performance, DL training strategies traditionally require extensive and diverse training datasets from various sites to be transferred and pooled into a "centralised location". However, such a data transferring process could raise practical concerns related to data security and patient privacy. Federated learning (FL) is a distributed collaborative learning paradigm which enables the coordination of multiple collaborators without the need for sharing confidential data. This distributed training approach has great potential to ensure data privacy among different institutions and reduce the potential risk of data leakage from data pooling or centralisation. This review article aims to introduce the concept of FL, provide current evidence of FL in ocular imaging, and discuss potential challenges as well as future applications.

A comprehensive survey on radio frequency (RF) fingerprinting: Traditional approaches, deep learning, and open challenges

Fifth generation (5G) network and beyond envision massive Internet of Things (IoT) rollout to support disruptive applications such as extended reality (XR), augmented/virtual reality (AR/VR), industrial automation, autonomous driving, and smart everything which brings together massive and diverse IoT devices occupying the radio frequency (RF) spectrum. Along with the spectrum crunch and throughput challenges, such a massive scale of wireless devices exposes unprecedented threat surfaces. RF fingerprinting is heralded as a candidate technology that can be combined with cryptographic and zero-trust security measures to ensure data privacy, confidentiality, and integrity in wireless networks. Motivated by the relevance of this subject in the future communication networks, in this work, we present a comprehensive survey of RF fingerprinting approaches ranging from a traditional view to the most recent deep learning (DL)-based algorithms. Existing surveys have mostly focused on a constrained presentation of the wireless fingerprinting approaches, however, many aspects remain untold. In this work, however, we mitigate this by addressing every aspect – background on signal intelligence (SIGINT), applications, relevant DL algorithms, systematic literature review of RF fingerprinting techniques spanning the past two decades, discussion on datasets, and potential research avenues – necessary to elucidate this topic to the reader in an encyclopedic manner.

Mutual-Supervised Federated Learning and Blockchain-Based IoT Data Sharing

Due to the decentralized, tamper-proof, and auditable properties of blockchain, more and more scholars and researchers are studying the application of blockchain technology in IoT data sharing. Federated learning is an effective way to enable data sharing, but can be compromised by dishonest data owners who may provide malicious models. In addition, dishonest data requesters may also infer private information from model parameters. To solve the above problems, a secure data sharing mechanism based on mutual-supervised federated learning and blockchain, BPCV-FL, is proposed. This mechanism ensures data privacy by adopting gradient descent algorithm with differential privacy protection in local model training and ensures the reliability of shared data through mutual supervision on the blockchain. Experimental results show that the proposed BPCV-FL has high accuracy and security in IoT data sharing.

HOPCTP: A Robust Channel Categorization Data Preservation Scheme for Industrial Healthcare Internet of Things

In this article, we present a robust channel categorization scheme to fix data privacy and preservation problems in an Industrial Healthcare Internet of Things (IHC-IoT) network. The proposed model categorizes the transmission bandwidth into four independent channels for each device by defining triggering rules with respect to time for reception and transmission of data. Besides, our developed prototype, which is known as high optimal path channel triggering protocol (HOPCTP) ensures data privacy and preservation utilizing minimal network resources in an IHC-IoT network. Furthermore, the HOPCTP prototype enables client-side devices to transmit and receive data with four different independent communication channels following the triggering mechanism to avoid adversary device anticipation in the network. The categorized channels are triggered with a defined time period to change their transmission and reception functionality, which triggers the transmitted data between different channels. Same data transmission via four different channels ensures the confidentiality and integrity of data because if an attacker captures one channel of data, he will not be able to understand the full message. The convalescent communication infrastructure is developed among patient wearable devices followed by cluster heads, micro base station, and macro base station to ensure data privacy and preservation with better communication metrics. In addition, the objective of the HOPCTP prototype is to resolve the data privacy issues in delay-sensitive applications, i.e., IHC-IoT networks. To achieve this, the HOPCTP prototype promotes data preservation and communication in terms of authenticity, congestion, throughput, communication cost, and packet loss ratio. The result statistics of the proposed scheme demonstrate remarkable improvement over the existing schemes for aforementioned comparative metrics.

Enhancing Cybersecurity and Privacy with Artificial Intelligence

The rapid evolution of technology has led to the proliferation of cyber threats and privacy concerns. This paper explores the integration of Artificial Intelligence (AI) in enhancing cybersecurity measures and ensuring data privacy

SAKAP: SGX-Based Authentication Key Agreement Protocol in IoT-Enabled Cloud Computing

With the rapid development of the Internet, Internet of Things (IoT) technology is widely used in people’s daily lives. As the number of IoT devices increases, the amount of data to be processed also increases. The emergence of cloud computing can process the data of IoT devices in a timely manner, and it provides robust storage and computing capabilities to facilitate data resource sharing. Since wireless communication networks are unstable and open, it is easy for attackers to eavesdrop, intercept, and tamper with the messages sent. In addition, authentication protocols designed for IoT-enabled cloud computing environments still face many security challenges. Therefore, to address these security issues, we propose an Intel software-guard-extensions (SGX)-based authentication key agreement protocol in an IoT-enabled cloud computing environment. The goal is to ensure data privacy and sustainable communication between the entities. Moreover, SGX can resist several well-known attacks. Finally, we show the security using the real-or-random model, ProVerif, and informal analysis. We also compare the security and performance of the proposed protocol with existing protocols. The comparison results show that our proposed protocol reduces the communication cost by 7.07% compared to the best one among the current protocols and ensures sufficient security.

Blockchain-Based Internet-of-Things for Secure Transmission of Medical Data in Rural Areas

Abstract Patients’ medical data are extremely sensitive information during storage and transfer, and it needs the highest security level. Furthermore, these records must frequently be linked to patient medical data, and then the linked medical data are securely transmitted to the healthcare center. In this study, a Blockchain-Based Traceable Data Sharing method is proposed to securely transfer the medical data. A Paillier homomorphic encryption method is used to prevent data theft or attacks from occurring in the cloud as a result of the transfer of medical data there. It prevents intravenous third parties, which executes arithmetic operations on the ciphertext. Then the encrypted data are stored in the cloud and to remove clone nodes in the gateway, a software-defined networking technology is introduced. Then a Blockchain-Based Traceable Data Sharing is proposed to ensure data privacy and authenticity while maintaining data privacy at the point of data transmission. Data are then encrypted using a new Enhanced Cipher Text-Policy Encryption Attribute-based Encryption (E-CP-ABE). Private blockchain transfers are carried out on the chain, supporting fine-grain access control with flexible access policies and creating a private key in E-CP-ABE. The presented technique is executed in Matlab software of version R2020a. The performance parameters are encryption, and decryption time, mean square error (MSE), peak signal-to-noise ratio (PSNR), sensitivity, respectively. The encryption process function is nearly 8% superior than the existing methods and the decryption time is 14% greater than other methods. As a result, this study shows that the research approach outperformed in terms of encryption time and decryption time, as well as PSNR, MSE, and sensitivity. This technique outperforms other state-of-the-art algorithms in terms of imperceptibility and robustness against various attacks. Consequently, this approach is more reliable than previous methods for the transmission of medical data.

The Effectiveness of IoT Based Wearable Devices and Potential Cybersecurity Risks: A Systematic Literature Review from the Last Decade

Wearable technology has enormous promise, particularly for data collection for cutting-edge health research, and its popularity has soared in recent years. This study aims to provide IoT-based wearable devices' effectiveness and potential cybersecurity threats to these innovative technologies. Using the PRISMA-2020 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology, we conducted a scoping study to understand better the application of inexpensive, consumer-grade wearables for health research from a population health viewpoint. Scopus databases yielded a total of 43 articles. Our findings show that academics and research participants are tremendously interested in this technology, but they are also wary about using wearables. These devices are still vulnerable to cybersecurity assaults such as data privacy and security in healthcare. However, blockchain technology is a potential alternative for integrating with IoT-based wearables to ensure data privacy. Furthermore, according to the findings, wearables have an increasingly diverse range of applications, including COVID-19 prediction, fertility tracking, heat-related sickness, pharmacological effects, and psychological therapies; they also include underrepresented populations, such as those with rare diseases. In low-resource environments, there is a lack of research on wearable devices.

Enhancing Security of Data in Cloud Storage Using Decentralized Infrastructure

Abstract: Nowadays, a considerable amount of data is stored in the cloud, which must be kept safe against unauthorised access. Various algorithms are employed to ensure data privacy and security. Every system's goal is to achieve confidentiality, integrity, and availability (CIA). The existing centralised cloud storage, on the other hand, does not have these CIA qualities. Decentralized cloud storage is used in conjunction with hash algorithm used to improve data security and storage approaches. It efficiently aids in the protection of data against manipulation or the deletion of a portion of data. A data connects the blocks that hold data in a block chain. Each block contains a hash value that is stored in the following block. As a result, the possibilities of data loss are reduced. Keywords: Decentralized Cloud Storage, SHA-512, Advance Encryption Standard, Encrypt, Decryp,CIA