Data Sharing Research Articles

Securely Cloud Data Storage and Sharing

With the increasing adoption of cloud computing, data storage and sharing have be-come integral parts of our digital lives. However, ensuring the security and privacy of data stored in the cloud remains a significant challenge. This paper proposes a novel approach for securely storing and sharing data in the cloud, addressing the vulnerabilities associated with traditional cloud storage models. The proposed approach utilizes advanced cryptographic techniques, including hyperchaotic encryption and hash functions, to protect the confidentiality and integrity of data stored in the cloud. The hyperchaotic encryption algorithm provides a high level of security by introducing chaos-based dynamics into the encryption process, making it resistant to various attacks. Additionally, the hash function ensures the integrity of data by generating unique identifiers for each file stored in the cloud. To enhance data sharing security, the proposed approach employs access control mechanisms and user authentication protocols. Access control rules are enforced to restrict unauthorized access to data, while user authentication ensures that only legitimate users can access and modify the shared data.

Secure Data Sharing Scheme for Multi-Node Devices

For the problem that the communication between maritime devices may be listened to by the adversary, resulting in the data shared between maritime devices may also be tampered with and forged by the adversary, thus jeopardizing the maritime IoT, this paper firstly starts from the wireless sensor network technology which is the most widely used in the maritime IoT, and then selects and designs a lightweight block cipher algorithm for realizing the secure data sharing of the multi-node devices in the maritime IoT. After analyzing the security of the algorithm, it is concluded that this paper's scheme performs well in terms of anti-differential analysis and linear analysis, and is better than PRESENT algorithm in terms of hardware implementation.

A new approach to a wicked problem: development of a cross-sector community-centered learning network to tackle childhood food inequity.

Food insecurity is a complex societal problem that disproportionately impacts households with children and those led by minoritized populations, with negative impacts on health across the life course. System to Achieve Food Equity adapted the learning systems model, used to address similarly complex problems, to tackle food insecurity at a neighborhood level. SAFE, born out of the COVID-19 pandemic, leverages a family-centered, community-based, cross-sector network fundamentally aimed at changing the food system so that all children in Cincinnati have the food they need to thrive. Through the following principles, Community-Led Network, Co-Production with Community, Equitable Sustainability, Learning to Learn Together, Distributing Leadership and Power, and Shared Data and Governance, SAFE has grown to over 300 individuals and 100 organizations, funded 9 novel interventions, distributed over 270,000 meals, and created a collaborative of motivated like-minded stakeholders. Future work includes improved data collection and sharing, support for increased stakeholder engagement and greater distribution of leadership and power, advocacy for policy change, refining measurement tools of network maturity for community settings, and collaboration with other efforts that contribute to food security indirectly.

Data Management Sharing Plan: Fostering Effective Trans-Disciplinary Communication in Collaborative Research

This paper scrutinizes the integral role of Trans-Disciplinary Communication (TDC) in conceptualizing and implementing Data Management and Sharing (DMS) plans in alignment with federal guidelines such as those from NIH and NSF. We begin by dissecting existing federal mandates for research data management, elucidating their pivotal role in maintaining data integrity and enabling accessibility. Using a case study as our methodological approach, we exemplify how TDC can effectively enhance the dialogue and practices surrounding DMS plans, fulfilling the requirements imposed by funding organizations. Furthermore, this research offers a novel contribution to the ongoing discourse by highlighting how DMS plans are foundational in promoting effective TDC and, by extension, advancing discussions in areas like Diversity Equity and Inclusion (DEI), STEM Education, and Collaboration.

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

Efficient User-Centric Privacy-Friendly and Flexible Wearable Data Aggregation and Sharing

1108 Sharing or not-sharing individual participant data: how valid are randomized trials evaluating induction of labor?

1108 Sharing or not-sharing individual participant data: how valid are randomized trials evaluating induction of labor?

Subject Review: Enhancing Cyber Security through Machine Learning Model in Cloud Computing Environment

Cloud computing (CC) has become more and more popular in environments that use distributed computing. Cloud-based processing and data storage is becoming a global trend. Perhaps might put up the effect that Software as a Service (SaaS) has on numerous commercial applications and daily lives as disruptive technology. Cloud computing (CC) is an extension of Internet-based computing, where devices can access shared software, resources, and data as required. Machine learning (ML) has become a popular tool in the areaof cybersecurity(CS)through the cloud computing environment in recent years. Examples of its applications include biometric-based user authentication and the detection of malware or intrusions. However, attacks on machine learning (ML) algorithms may occur in both the testing and training phases, which typically results in significant performance reductions and safety breaches. This article provides a comprehensive study on the use of machine learning (ML) algorithmsto enhance cybersecurity (CS), as well as a comparison of most of the machine learning (ML) techniques mentioned in the work. The article presents future work of previous works that help researchers find ways to improve cybersecurity.

Verifiable and Redactable Blockchain for Internet of Vehicles Data Sharing

Verifiable and Redactable Blockchain for Internet of Vehicles Data Sharing

APDPFL: Anti-Poisoning Attack Decentralized Privacy Enhanced Federated Learning Scheme for Flight Operation Data Sharing

APDPFL: Anti-Poisoning Attack Decentralized Privacy Enhanced Federated Learning Scheme for Flight Operation Data Sharing

The formation pattern, causes, and governance of network public opinion on university emergencies.

University emergencies, garnering significant public attention and shaping network opinions, pose a crucial challenge to universities' management and societal stability. Hence, network public opinion on university emergencies is a vital issue. Nevertheless, the underlying mechanism has not been fully explored and cannot be efficiently controlled. This study aimed to explore the formation pattern of network public opinion on university emergencies, analyze its causes, and provide scientific governance strategies for coping with this issue. Based on a sample set of 204 cases from the Zhiwei Data Sharing Platform, this study classifies network public opinion on university emergencies into six types and visually analyzes their characteristics: time distribution, subject, duration, and emotion. By integrating the theory of the network public opinion field, this study develops a network public opinion field model of university emergencies to reveal its formation pattern. Furthermore, it analyzes the causes of network public opinion on university emergencies from the perspective of the public opinion lifecycle and proposes corresponding governance strategies. The sample consisted of 304 cases of real-life public opinion, and the visualization results show that public opinion on mental health and teacher-student safety constitutes the predominant types, accounting for 83.3%. High-occurrence subjects are public universities (88.24%) and students (48%). The most frequent months are July and December. 90.20% of the public opinions have a lifespan of less than 19 days, with an impact index ranging from 40 to 80. The public's emotional response to different types of public opinion varies, with negative emotions dominating. This study provides novel insights for understanding their formation and dissemination. It also provides practical implications for relevant departments to govern network public opinion on university emergencies.

Wasserstein Generative Adversarial Networks Based Differential Privacy Metaverse Data Sharing.

Although differential privacy metaverse data sharing can avoid privacy leakage of sensitive data, randomly perturbing local metaverse data will lead to an imbalance between utility and privacy. Therefore, this work proposed models and algorithms of differential privacy metaverse data sharing using Wasserstein generative adversarial networks (WGAN). Firstly, this study constructed the mathematical model of differential privacy metaverse data sharing by introducing appropriate regularization term related to generated data's discriminant probability into WGAN. Secondly, we established basic model and algorithm for differential privacy metaverse data sharing using WGAN based on the constructed mathematical model, and theoretically analyzed basic algorithm. Thirdly, we established federated model and algorithm for differential privacy metaverse data sharing using WGAN by serialized training based on basic model, and theoretically analyzed federated algorithm. Finally, based on utility and privacy metrics, we conducted a comparative analysis for the basic algorithm of differential privacy metaverse data sharing using WGAN, and experimental results validate theoretical results, which show that algorithms of differential privacy metaverse data sharing using WGAN maintaining equilibrium between privacy and utility.

A Secure Medical Data Sharing Framework for Fight Against Pandemics Like Covid-19 by Using Public Blockchain

A Secure Medical Data Sharing Framework for Fight Against Pandemics Like Covid-19 by Using Public Blockchain

‘Does Sharing mean Caring?’ – Responsibility and Governance in Health Data Sharing

‘Does Sharing mean Caring?’ – Responsibility and Governance in Health Data Sharing

A Survey of Proxy Re-Encryption for Secure Data Sharing in Cloud Computing

Never before have data sharing been more convenient with the rapid development and wide adoption of cloud computing. However, how to ensure the cloud user’s data security is becoming the main obstacles that hinder cloud computing from extensive adoption. Proxy re-encryption serves as a promising solution to secure the data sharing in the cloud computing. It enables a data owner to encrypt shared data in cloud under its own public key, which is further transformed by a semitrusted cloud server into an encryption intended for the legitimate recipient for access control. This paper gives a solid and inspiring survey of proxy re-encryption from different perspectives to offer a better understanding of this primitive. In particular, we reviewed the state-of-the-art of the proxy re-encryption by investigating the design philosophy, examining the security models and comparing the efficiency and security proofs of existing schemes. Furthermore, the potential applications and extensions of proxy re-encryption have also been discussed. Finally, this paper is concluded with a summary of the possible future work.

Overview of Sharing Data in Intelligent Transportation System

Overview of Sharing Data in Intelligent Transportation System

Research on the Construction and Effectiveness of Teaching Resource Sharing Platform for Art and Design Majors

Abstract In the information society, the sharing characteristics of Internet information provide students with rich teaching resources. This study integrates Hadoop technology with the teaching resource sharing system for art and design majors in colleges and universities. It builds an open-source parallelised computing and distributed architecture based on the traditional B/S model, using the distributed file system HDFS to store massive teaching resources. On this basis, MapReduce, a parallelised programming model, is used to provide parallel computing retrieval functions, and the innovative contract technology of blockchain is introduced to store data-heavy shared teaching resources in the underlying IPFS to ensure the security and trustworthiness of shared data. The results show that under the blockchain environment, the retrieval time of teaching resources is as low as 12ms only, and the Gas value consumed by the operation of storing data totals 0.1385 ETH, which meets the performance requirements of the platform and provides a new practical solution for sharing teaching resources.

Can we profit from BigTechs' time series models in predicting earnings per share? Evidence from Poland

<abstract> <p>Forecasting earnings for publicly traded companies is of paramount significance for investments, which is the background of this research. This holds particularly true in emerging markets where the coverage of these companies by financial analysts' predictions is limited. This research investigation delves into the prediction inaccuracies of cutting-edge time series forecasting algorithms created by major technology companies such as Facebook, LinkedIn, Amazon, and Google. These techniques are employed to analyze earnings per share data for publicly traded Polish companies during the period spanning from the financial crisis to the pandemic shock. My objective was to compare prediction errors of analyzed models, using scientifically defined error measures and a series of statistical tests. The seasonal random walk model demonstrated the lowest error of prediction, which might be attributable to the overfitting of complex models.</p> </abstract>

Design and Planning of Tourism Path Based on Social Media Sharing Data Mining

Abstract In recent years, along with the rapid development of artificial intelligence, big data and social media, informatization in the tourism industry also shows an explosive trend. This paper constructs a tourism path planning system based on data mining technology and the selection method of the optimal path. The GS algorithm is used to optimize the SVM algorithm to form the GS-SVM fusion algorithm, which makes the tourism path planning and predicts the optimal path according to the specific conditions of the journey, the characteristics of the scenic spot itself, and the tourists’ needs. After testing, this system has a good prediction performance on the traffic accessibility, attraction congestion and crowd change of scenic tour path. It is found that the transportation accessibility of scenic tour paths is positively correlated with tourists’ experience. In addition, in the experiment on the advantages and disadvantages of tourism paths, the passage time of paths 14, 15 and 16 is more than 3 minutes. Still, the actual length of these three paths is not more than 350m, which indicates that there are things for tourists to visit and experience on the passage paths, thus lengthening the passage time. This shows that the system provides real-time and reference paths for tourists by mining social media sharing data.

Constant-Round Private Decision Tree Evaluation for Secret Shared Data

Decision tree evaluation is extensively used in machine learning to construct accurate classification models. Often in the cloud-assisted communication paradigm cloud servers execute remote evaluations of classification models using clients' data. In this setting, the need for private decision tree evaluation (PDTE) has emerged to guarantee no leakage of information for the client's input nor the service provider's trained model i.e., decision tree. In this paper, we propose a private decision tree evaluation protocol based on the three-party replicated secret sharing (RSS) scheme. This enables us to securely classify inputs without any leakage of the provided input or the trained decision tree model. Our protocol only requires constant rounds of communication among servers, which is useful in a network with longer delays.Ma et al. (NDSS 2021) presented a lightweight PDTE protocol with sublinear communication cost with linear round complexity in the size of the input data. This protocol works well in the low latency network such as LAN while its total execution time is unfavourably increased in the WAN setting. In contrast, Tsuchida et al. (ProvSec 2020) constructed a constant round PDTE protocol at the cost of communication complexity, which works well in the WAN setting. Although their construction still requires 25 rounds, it showed a possible direction on how to make constant round PDTE protocols. Ji et al. (IEEE Transactions on Dependable and Secure Computing) presented a simplified PDTE with constant rounds using the function secret sharing (FSS) at the cost of communication complexity. Our proposed protocol only requires five rounds among the employed three servers executing secret sharing schemes, which is comparable to previously proposed protocols that are based on garbled circuits and homomorphic encryption. To further demonstrate the efficiency of our protocol, we evaluated it using real-world classification datasets. The evaluation results indicate that our protocol provides better concrete performance in the WAN setting that has a large network delay.

Lightweight Access Delegation With Multi-Ciphertext Equivalence Test for Shared Data in ITS

Lightweight Access Delegation With Multi-Ciphertext Equivalence Test for Shared Data in ITS