Data Owners Research Articles

Enhancing Cloud Storage Security in Big Data: A Comprehensive Data Sharing Protocol

A cloud based system gives advantage of a storage facility provided by a cloud service provider for exchange of data with authorised users. cloud providers store shared data in huge data centres outside the data owner's trust zone, which may raise the issue of data confidentiality. Number of parties, devices, app in cloud increases so number of access point also increases and making perfect access access control is challenging, so cloud data subject to being deleted or can be modified by cloud service provider. This article offers a secret sharing group key management protocol (SSGK) to prevent unwanted access in the communication process and shared data. in SSGK, group key is utilised to encrypt the shared data, and a secret sharing mechanism is employed to distribute the group key. security and performance evaluations show that our approach significantly reduces the security and privacy concerns of data sharing in cloud storage. Keywords : Big data, Security and Privacy, Cloud Storage, Data Sharing, Encryption, Decryption.

EHealth Innovation for Chronic Obstructive Pulmonary Disease: A Context-Aware Comprehensive Framework

Chronic Obstructive Pulmonary Disease (COPD) poses a significant global healthcare challenge. It is a progressive lung disease that causes breathing difficulties and can significantly impact a person's quality of life. COPD is primarily caused by smoking, but other factors, such as air pollution and genetic predisposition, can also contribute to its development. This paper introduces a novel Context-Aware Framework for the Diagnosis and Personalized Management of COPD. We discuss the limitations of traditional COPD management, highlighting the importance of early detection and remote monitoring. Early detection and remote monitoring are crucial in managing COPD as they allow for timely interventions and better disease management. In this paper, we propose a framework based mostly on contextual data and other parameters of COPD as put forth by the World Health Organization (WHO) in the form of the International Classification of Functioning, Disability, and Health. Ontologies drive this architecture and incorporate dynamic contextual information from patient environments, user profiles, and sensor data. In addition to the various obvious data items like patient personal details (gender, contact, medical history) and COPD risks and symptoms, the COPD ontology also considers the details about the caregiver and healthcare professional. This is in addition to the contextual data processed separately using the Context Ontology. The ontology we constructed using Protégé serves as the framework for the structured representation and logical inference of contextual information. By harnessing dynamic contextual data, our ontology enables real-time decision-making tailored to individual patient requirements. It empowers healthcare professionals to make informed choices and deliver timely interventions, enhancing healthcare services by offering proactive care to detect early signs of health deterioration and suggest preventive measures. This approach improves patient experiences and optimizes resource allocation within the healthcare system. To uphold ethical standards and prioritize the needs of patients, we emphasize the significance of safeguarding data, obtaining informed permission, and recognizing data ownership. The ontology-based approach presented in this study offers a scalable and flexible framework that can be readily incorporated into existing healthcare systems, redefining the management of COPD in response to evolving demands. Security poses one of the biggest threats in context-based environments due to the different data formats acquired by the diverse sensors. Another essential consideration is confidentiality because the data in hand is sensitive patient information.

Making apps, owning data: Digital sovereignty and public authorities’ arrangements to “byte” back

This study tells the story of two public transport authorities (PTA) in Sweden who developed their own apps. Although this might seem trivial and far-detached from the critical issues discussed in organization studies, this story raises questions of great relevance for this field, namely how digital sovereignty is organized. Digital sovereignty refers to governments trying to take or regain power and control over “the digital,” which a small group of large tech companies have monopolized. Drawing on a 3 year qualitative study of app making, and using assemblage theory, this study shows that digital sovereignty is not only about controlling software development or data ownership, but also about re-configuring the organization in relation to digital artifacts such as apps. By bypassing procurement procedures and paralleling their IT-departments, the PTAs here display how digital sovereignty comes from “below,” originating from frustrated civil servants within the public authorities who literally tried to “byte back” in a digital world run by large tech companies.

Provably secure data selective sharing scheme with cloud-based decentralized trust management systems

AbstractThe smart collection and sharing of data is an important part of cloud-based systems, since huge amounts of data are being created all the time. This feature allows users to distribute data to particular recipients, while also allowing data proprietors to selectively grant access to their data to users. Ensuring data security and privacy is a formidable task when selective data is acquired and exchanged. One potential issue that emerges is the risk that data may be transmitted by cloud servers to unauthorized users or individuals who have no interest in the particular data or user interests. The prior research lacks comprehensive solutions for balancing security, privacy, and usability in secure data selective sharing schemes inside Cloud-Based decentralized trust management systems. Motivating factors for settling this gap contain growing concerns concerning data privacy, the necessity for scalable and interoperable frameworks, and the increasing dependency on cloud services for data storage and sharing, which necessitates robust and user-friendly mechanisms for secure data management. An effective and obviously secure data selective sharing and acquisition mechanism for cloud-based systems is proposed in this work. We specifically start by important a common problematic related to the selective collection and distribution of data in cloud-based systems. To address these issues, this study proposes a Cloud-based Decentralized Trust Management System (DTMS)-connected Efficient, Provably Secure Data Selection Sharing Scheme (EPSDSS). The EPSDSS approach employs attribute-based encryption (ABE) and proxy re-encryption (PRE) to provide fine-grained access control over shared data. A decentralized trust management system provides participant dependability and accountability while mitigating the dangers of centralized trust models. The EPSDSS-PRE paradigm would allow data owners to regulate granular access while allowing users to customize data collection without disclosing their preferences. In our strategy, the EPSDSS recognizes shared data and generates short fingerprints for information that can elude detection before cloud storage. DTMS also computes user trustworthiness and improves user behaviour administration. Our research demonstrates that it’s able to deliver trustworthy and safe data sharing features in cloud-based environments, making it a viable option for enterprises seeking to protect sensitive data while maximizing collaboration and utilization of resources.

Multiple Keyword Search in Cloud Data

Cloud computing is often regarded as a great innovation due to the increasing demand for endless storage space and reliable retrieval services. Several works have been developed on ranked multi-keyword search over cloud data with numerous data owners concerned about privacy. However, most of these methods can be broken by a clever attacker using a combination of a keyword guess and an equivalency test. Also, when searching across data from many data owners, they can't reliably return the top k results to the users. The unreliability of search results and the potential for the exposure of critical keyword information are two obvious drawbacks. To address this issue, we first propose a novel efficient ranked multi-keyword retrieval scheme with keyword privacy for multiple data owners, allowing the cloud server to perform multikeyword search over the cloud data and then return the top-k ranked search results to data users without leaking any keyword and trapdoor information. Furthermore, we demonstrate through stringent security analysis that our method is safe from both internal and external threats. Finally, the results of the performance evaluation show that our scheme outperforms other ranked multi-keyword search systems. Keywords: cloud computing, multi-keyword retrieval, multiple data owner model, privacy sensitive, ranked.

Indigenous Data Sovereignty: What Can Yarning Teach Us?

ABSTRACT Yarning is a trusted, culturally integral way of creating new knowledge that is different from focus groups. This article is a reflection piece from an Aboriginal Researcher engaging with Indigenous Standpoint and auto-ethnographic approaches to explore how yarning interfaces with Indigenous data sovereignty. It is argued through the themes of deep listening, tension, relationality, and power—from memories and lived experiences—that yarning upholds the rights of Indigenous data sovereignty because it enables intimacy, connection, and recognition of cultural knowledge holders. IMPLICATIONS Indigenous Data Sovereignty recognises the rights of Indigenous people to determine the means of collection, ownership, and dissemination of data pertaining to Indigenous Peoples. The level of intimacy and connection between First Nations Peoples obtained through yarning is at risk of being compromised if non-Indigenous researchers conflate yarning with focus groups.

Secure Date Storage Using ECC Encryption with Verifiable Data Sharing in Cloud

Cloud data storage has become increasingly popular, but the security and privacy of data stored in the cloud remain major concerns. Cloud computing is the most feasible approach for implementing Digital Twin services since it has prodigious advantages. It provides on demand services, computing resources, ubiquitous network access, etc., making it suitable for the next-generation information technology architecture. In cloud-assisted data storage environments, the data owners generate data from physical assets and disseminate it to the cloud server.

Practical multi-party private set intersection cardinality and intersection-sum protocols under arbitrary collusion1

Private set intersection cardinality (PSI-CA) and private intersection-sum with cardinality (PSI-CA-sum) are two primitives that enable data owners to learn the intersection cardinality of their data sets, with the difference that PSI-CA-sum additionally outputs the sum of the associated integer values of all the data that belongs to the intersection (i.e., intersection-sum). However, to the best of our knowledge, all existing multi-party PSI-CA (MPSI-CA) protocols are either limited by high computational cost or face security challenges under arbitrary collusion. As for multi-party PSI-CA-sum (MPSI-CA-sum), there is even no formalization for this notion at present, not to mention secure constructions for it. In this paper, we first present an efficient MPSI-CA protocol with two non-colluding parties. This protocol significantly decreases the number of parties involved in expensive interactive procedures, leading to a significant enhancement in runtime efficiency. Our numeric results demonstrate that the running time of this protocol is merely one-quarter of the time required by our proposed MPSI-CA protocol that is secure against arbitrary collusion. Therefore, in scenarios where performance is a priority, this protocol stands out as an excellent choice. Second, we successfully construct the first MPSI-CA protocol that achieves simultaneous practicality and security against arbitrary collusion. Additionally, we also conduct implementation to verify its practicality (while the previous results under arbitrary collusion only present theoretical analysis of performance, lacking real implementation). Numeric results show that by shifting the costly operations to an offline phase, the online computation can be completed in just 12.805 seconds, even in the dishonest majority setting, where 15 parties each hold a set of size 2 16 . Third, we formalize the concept of MPSI-CA-sum and present the first realization that ensures simultaneous practicality and security against arbitrary collusion. The computational complexity of this protocol is roughly twice that of our MPSI-CA protocol. Besides the main results, we introduce the concepts and efficient constructions of two novel building blocks: multi-party secret-shared shuffle and multi-party oblivious zero-sum check, which may be of independent interest.

Implications of AI in land management

This study sought to find out implications of Artificial Intelligence (AI) in land management. Artificial Intelligence (AI) offers significant benefits for land development and decision-making, ethical concerns regarding data privacy, bias, and social impact necessitate frameworks to ensure its responsible application. The increasing use of AI models in land management raises ethical concerns about data ownership, privacy, algorithmic bias, environmental impact, and social displacement. Traditional research ethics frameworks may not be sufficient for AI-driven land management practices.  This paper examines critical issues arising from data governance, algorithmic transparency, and environmental and social impact assessment. It proposes frameworks that consider established research ethics principles and advocate for community engagement.  The paper highlights the potential for AI to contribute to sustainable and equitable land management. However, it also identifies potential negative consequences such as job losses, unequal access to technology, and exacerbation of existing social divides.  The paper emphasizes the need for collaboration between researchers, policymakers, communities, and developers to construct ethical frameworks that ensure AI contributes to sustainable land management practices.  These frameworks should address data governance, algorithmic transparency, environmental and social impact assessments, and community engagement.

SDACS: Blockchain-Based Secure and Dynamic Access Control Scheme for Internet of Things.

With the rapid growth of the Internet of Things (IoT), massive terminal devices are connected to the network, generating a large amount of IoT data. The reliable sharing of IoT data is crucial for fields such as smart home and healthcare, as it promotes the intelligence of the IoT and provides faster problem solutions. Traditional data sharing schemes usually rely on a trusted centralized server to achieve each attempted access from users to data, which faces serious challenges of a single point of failure, low reliability, and an opaque access process in current IoT environments. To address these disadvantages, we propose a secure and dynamic access control scheme for the IoT, named SDACS, which enables data owners to achieve decentralized and fine-grained access control in an auditable and reliable way. For access control, attribute-based control (ABAC), Hyperledger Fabric, and interplanetary file system (IPFS) were used, with four kinds of access control contracts deployed on blockchain to coordinate and implement access policies. Additionally, a lightweight, certificateless authentication protocol was proposed to minimize the disclosure of identity information and ensure the double-layer protection of data through secure off-chain identity authentication and message transmission. The experimental and theoretical analysis demonstrated that our scheme can maintain high throughput while achieving high security and stability in IoT data security sharing scenarios.

A cloud‐based solution for trustless indigenous data sovereignty: Protecting Māori biodiversity management data in Aotearoa New Zealand

AbstractIndigenous peoples should be able to govern data about themselves, their territories, resources, and ways of life, collected by themselves or others. However, the progressive use of cloud computing for the geoweb raises data security and privacy concerns. We propose a complete and trustless approach for indigenous geospatial data sovereignty on the cloud by furnishing security functions at the core—the web browser. Geomasking permits sharing an anonymized dataset with less privileged users, while the original is protected and shared with sovereign data owners via public‐key encryption. The encrypted dataset's hash value is notarized on the blockchain for the verification of its authenticity when on the cloud. The application was designed for the protection of Biodiversity Management Areas stewarded by the Māori people in Aotearoa New Zealand. It enables diversified functions of geospatial data protection compared with previous works focusing on the cloud by solving data‐sharing problems without relying on a third party.

ReActHE: A homomorphic encryption friendly deep neural network for privacy-preserving biomedical prediction

The growing distribution of deep learning models to individuals’ devices on sensitive healthcare data introduces challenging privacy and security problems when computation is being operated on an untrusted server. Homomorphic encryption (HE) is one of the appropriate cryptographic techniques to provide secure machine learning computation by directly computing over encrypted data, so that allows the data owner and model owner to outsource processing of sensitive information to an untrusted server without leaking any information about the data. However, most current HE schemes only support limited arithmetic operations, which significantly hinder their applications to implement a secure deep learning algorithm, especially on the nonlinear activation function of a deep neural network. In this paper, we develop a novel HE-friendly deep neural network, named REsidue ACTivation HE (ReActHE), to implement a precise and privacy-preserving algorithm with a non-approximating HE scheme on the activation function. We consider a residue activation strategy with a scaled power activation function in a deep neural network for HE-friendly nonlinear activation. Moreover, we propose a residue activation network structure to constrain the latent space in the training process to alleviate the optimization difficulty. We comprehensively evaluate the proposed ReActHE method using various biomedical datasets and widely-used image datasets. Our results demonstrate that ReActHE outperforms other alternative solutions to secure machine learning with HE and achieves low approximation errors in classification and regression tasks.

Mobile Collaborative Learning Over Opportunistic Internet of Vehicles

Machine learning models are widely applied for vehicular applications, which are essential to future intelligent transportation system (ITS). Traditional model training methods commonly employ a client-server architecture to perform local training and global iterative aggregations, which can consume significant bandwidth resources that are often absent in vehicular networks, especially in high vehicle density scenarios. Modern vehicle users naturally can collaboratively train machine learning models as they are the data owner and have strong local computing power from the onboard units (OBU). In this paper, we propose a novel collaborative learning scheme for mobile vehicles that can utilize the opportunistic vehicle-to-roadside (V2R) communication to exploit the common priors of vehicular data without interaction with a centralized coordinator. Specifically, vehicles perform local training during the driving journey, and simply upload its local model to roadside unit (RSU) encountered on the way. RSU's model will be updated accordingly and sent back to the vehicle via the V2R communication. We have theoretically shown that RSUs' models can eventually converge without a backhaul connection. Extensive experiments upon various road configurations demonstrate that the proposed scheme can efficiently train models among vehicles without dedicated Internet access and scale well with both the road range and vehicle density.

FedCAE: A New Federated Learning Framework for Edge-Cloud Collaboration Based Machine Fault Diagnosis

With the coming of the industrial big data era, data-driven fault diagnosis models emerge recently and show potential results in many studies. However, it is impractical to collect massive high-quality data in actual industrial applications, which strongly reduces the performance of fault diagnosis models. Therefore, multiple data owners are motivated to share their own data for training a brilliant model with the aggregated dataset. Notwithstanding, it is unrealistic to share data due to the potential conflict of interest. For addressing the issue of data island, a new federated framework namely FedCAE is proposed for fault diagnosis. In this framework, each client is equipped with a convolutional autoencoder (CAE) trained by its corresponding local data. Then, all CAEs are uploaded to a server and aggregated to a global CAE according to an adaptive weighted selection mechanism. Afterwards, the global CAE is downloaded to all clients for extracting low-level features from local datasets by its encoder and uploading these features to train a global fault diagnosis classifier in the server. At last, the classifier is downloaded to all clients for completing their own diagnosis tasks. Experiment results on two bearing datasets suggest FedCAE offers a promising solution on confidential decentralized learning.

Biometric identification on the cloud: A more secure and faster construction

Cloud-assisted biometric identification can benefit data owners with limited resources by offloading their storage and identification tasks to cloud servers with abundant resources. However, the adoption of this computing paradigm is hindered by security and privacy concerns. To solve the dilemma, several privacy-preserving cloud-assisted biometric identification protocols have been proposed. Nevertheless, the existing designs suffer from one or more of the following restrictions: (1) support only for data owner-online query, (2) low security level, and (3) inefficiency. Motivated by these challenges, this paper aims to explore a more secure and efficient construction. Concretely, we initialize the error weighted hashing (EWH)-based data packing technique to replace the time-consuming linear scan search during the identification process. Additionally, to ensure compatibility with the EWH-based search algorithm, we formulate an identity (id)-based scoring strategy and introduce a random split-based data encryption algorithm, which circumvents the costly homomorphic encryption scheme. Within the framework of the two non-colluding servers model, the random split-based one-time pad encryption guarantees provably indistinguishable security against chosen-plaintext attacks. Finally, since only simple hash and xor operations are involved, our novel construction exhibits significantly improved performance compared to prior designs, both in theory and in practice. This superiority is validated through rigorous theoretical analysis and extensive experimental evaluation.

Delegated Private Matching For Compute

Private matching for compute (PMC) establishes a match between two datasets owned by mutually distrusted parties (C and P) and allows the parties to input more data for the matched records for arbitrary downstream secure computation without rerunning the private matching component. The state-of-the-art PMC protocols only support two parties and assume that both parties can participate in computationally intensive secure computation. We observe that such operational overhead limits the adoption of these protocols to solely powerful entities as small data owners or devices with minimal computing power will not be able to participate. We introduce two protocols to delegate PMC from party P to untrusted cloud servers, called delegates, allowing multiple smaller P parties to provide inputs containing identifiers and associated values. Our Delegated Private Matching for Compute protocols, called DPMC and DsPMC, establish a join between the datasets of party C and multiple delegators P based on multiple identifiers and compute secret shares of associated values for the identifiers that the parties have in common. We introduce a rerandomizable encrypted oblivious pseudorandom function (OPRF) primitive, called EO, which allows two parties to encrypt, mask, and shuffle their data. Note that EO may be of independent interest. Our DsPMC protocol limits the leakages of DPMC by combining our EO scheme and secure three-party shuffling. Finally, our implementation demonstrates the efficiency of our constructions by outperforming related works by approximately 10x for the total protocol execution and by at least 20x for the computation on the delegators.

Право собственности на информационные объекты: проблемы понимания и особенности реализации

В статье рассматривается один из наиболее актуальных вопросов – понимание права собственности. Восприятие информации и информационных продуктов в качестве объектов гражданских правоотношений повлекло за собой возникновение большого количества споров о возможности дальнейшего применения традиционной конструкции права собственности, которая в силу своей вещной природы в складывающихся жизненных обстоятельствах утрачивает универсальность и нуждается в переосмыслении. В рамках статьи поставлены вопросы понимания права собственности и его конструктивных элементов, а также правового статуса информационных объектов, проведен анализ допустимости и обоснованности применения традиционной конструкции права собственности к информационным объектам. Сделан вывод о схожей правовой конструкции классического права собственности и правового режима, необходимого для применения к информационным объектам. The article deals with one of the most pressing issues of modern understanding of property law. The perception of information and information products as objects of civil relations has led to the emergence of a large number of disputes about the possibility of further application of the traditional construction of property rights, which, due to its material nature, loses its universality in the current life circumstances and needs to be rethought. The article raises questions of understanding property rights and its structural elements, as well as the legal status of information objects, analyzes the admissibility and validity of applying the traditional construction of property rights to information objects. The conclusion is made about the similar legal structure of the classical property right and the legal regime necessary for application to information objects.

Enhancing secure multi-group data sharing through integration of IPFS and hyperledger fabric

Data sharing is increasingly important across various industries. However, issues such as data integrity verification during sharing, encryption key leakage, and difficulty sharing data between different user groups have been identified. To address these challenges, this study proposes a multi-group data sharing network model based on Consortium Blockchain and IPFS for P2P sharing. This model uses a dynamic key encryption algorithm to provide secure data sharing, avoiding the problems associated with existing data transmission techniques such as key cracking or data leakage due to low security and reliability. Additionally, the model establishes an IPFS network for users within the group, allowing for the generation of data probes to verify data integrity, and the use of the Fabric network to record log information and probe data related to data operations and encryption. Data owners retain full control over access to their data to ensure privacy and security. The experimental results show that the system proposed in this study has wide applicability.

Enhancing Secure and Reliable Data Transfer through Robust Integrity

Cloud computing has emerged as a highly efficient platform that allows multiple users to access various services through virtualization on a shared physical network. The participants in a Cloud Computing (CC) environment include Cloud Service Providers (CSP), Consumers, Brokers, and Auditors. The advantages of cloud storage, such as universal network access, convenience, and scalability, have led to data owners preferring to store their data on remote servers. However, the transfer of outsourced data has become a critical requirement for cloud users due to the availability of different cloud storage services with varying quality of services.
 One major challenge in this context is ensuring the security of secret keys and data integrity. There is no guarantee of data integrity when storing data on an untrusted cloud server. To address this issue, this paper proposes a secure and efficient data integrity verification scheme for cloud storage services. The scheme utilizes a key-homomorphic cryptographic primitive to reduce system complexity and eliminate the need for a public key authentication framework based on a public key infrastructure (PKI) in the data integrity checking protocol. By employing this approach, the proposed method ensures the integrity of remote data stored on cloud servers. Through security analysis and empirical evaluation, it is demonstrated that our scheme is both practical and effective for securely sharing records with multiple owners in cloud computing.

Ethische Aspekte von Blockchain-Technologien in der biomedizinischen Forschung

Abstract Definition of the problem Biomedical research based on big data offers immense benefits. Large multisite research that integrates large amounts of personal health data, especially genomic and genetic data, might contribute to a more personalized medicine. This type of research requires the transfer and storage of highly sensitive data, which raises the question of how to protect data subjects against data harm, such as privacy breach, disempowerment, disenfranchisement, and exploitation. As a result, there is a trade-off between reaping the benefits of big-data-based biomedical research and protecting data subjects’ right to informational privacy. Arguments Blockchain technologies are often discussed as a technical fix for the abovementioned trade-off due to their specific features, namely data provenance, decentralization, immutability, and access and governance system. However, implementing blockchain technologies in biomedical research also raises questions regarding consent, legal frameworks, and workflow integration. Hence, accompanying measures, which I call enablers, are necessary to unleash the potential of blockchain technologies. These enablers are innovative models of consent, data ownership models, and regulatory models. Conclusion Blockchain technologies as a technical fix alone is insufficient to resolve the aforementioned trade-off. Combining this technical fix with the enablers outlined above might be the best way to perform biomedical research based on big data and at the same time protect the informational privacy of data subjects.