Data Owners Research Articles

Innovating Cybersecurity Education Through AI-augmented Teaching

In traditional teaching frameworks, instructors face significant obstacles in offering current and synchronized learning materials and examples, especially when the course is taught by multiple instructors. This situation can affect the quality of the course's learning outcomes. These challenges become more pronounced in today’s higher education, because of the heightened complexity arising from the need to cover a range of course materials, diverse student backgrounds, varying skill levels, and different student expectations—all within the constraints of a fixed teaching and learning schedule. Furthermore, due to resource constraints, not every instructor has the availability of a teaching assistant (TA). Especially, while the demand for cybersecurity continues to rise, the dynamic nature of the cybersecurity field leads to the frequent emergence of new issues and incidents. To address these challenges, we examine the capabilities of generative AI to innovate teaching techniques and methods for cybersecurity curricula. We further explore the novel challenges introduced by generative AI, including issues related to privacy, data ownership, transparency, and other associated concerns, underscoring the need for comprehensive solutions. Our work further examines the teaching and learning capabilities of dynamically generated, up-to-date class materials in a personalized study environment augmented by AI. The adaptability of AI-augmented teaching across various disciplines will bring innovation to higher

Protecting Sensitive Data with Secure Data Enclaves

A Secure Data Enclave is a system that allows data owners, such as governments and private firms, to control data access and ensure data security while facilitating approved uses of data by other parties. This model of data use offers additional protections and technical controls for the data owner compared to the more commonly used approach of transferring data from the owner to another party through a data sharing agreement. Under the data use model, the data owner retains full transparency and auditing over the other party's access, which can be difficult to achieve in practice with even the best legal instrument for data sharing. We describe the key technical requirements for a Secure Data Enclave, provide a reference architecture for its implementation on Amazon Web Services using managed cloud services, and describe four use cases of this architecture in partnerships with state governments to control access to sensitive administrative data.

Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain

As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense methods proposed to mitigate such concerns, it is challenging to balance adverse effects while allowing that each credible node contributes to the learning process. To this end, a Fair and Robust FL method is proposed for defense against model poisoning attack from malicious nodes, namely FRFL. FRFL can learn a high-quality model even if some nodes are malicious. In particular, we first classify each participant into three categories: training node, validation node, and blockchain node. Among these, blockchain nodes replace the central server in classic FL methods while enabling secure aggregation. Then, a fairness-aware role rotation method is proposed to periodically alter the sets of training and validation nodes in order to utilize the valuable information included in local datasets of credible nodes. Finally, a decentralized and adaptive aggregation mechanism cooperating with blockchain nodes is designed to detect and discard malicious nodes and produce a high-quality model. The results show the effectiveness of FRFL in enhancing model performance while defending against malicious nodes.

Review on Verifiable Outsourcing of Industrial Internet Data in Securely Shared

With the rapid development of information technology, industrial Internet has also entered a rapid development stage. As cloud servers have huge storage space as well as powerful computing power, more and more resource-constrained factories outsource their data to cloud servers for computation and storage to reduce the burden of data owners. Industrial data contains information about industrially produced products, and the high value of such information attracts attacks from illegal users, which may lead to data leakage. In addition, cloud servers are not fully trustworthy; therefore, it is important to ensure the security and correctness of industrial Internet data during outsourced computation and storage for the healthy development of the industrial Internet. In this paper, we explore technical solutions to guarantee the security and correctness of industrial Internet data when outsourcing computation, to achieve the privacy protection of industrial Internet data.

Platform Law as EU Law

Abstract Digital platforms have become the subject of several legal acts within a relatively short period of time. The emerging European platform law faces particular challenges due to three specificities of platforms. First, the area to be regulated consists of the same platforms operating throughout the Union with a more or less uniform service in all Member States and beyond the Union. Second, the fact that the platforms can be used for almost unlimited purposes, whereas the Union does not have unlimited regulatory powers. Third, there is the dynamic development of the platform economy, which constantly presents us with new phenomena and potential risks, giving operators a knowledge advantage over regulators and generating a need for private ordering by platforms themselves. In this combination, the legal challenges differ from the issues addressed by most other acts of the EU Digital Strategy: for example, the Data Governance Act focuses not on private but on public data owners, while the Data Act focuses on Internet of Things applications, which are not used as ubiquitously as digital platforms. Because of these particularities, an overarching view of the phenomena of platform regulation helps to provide a background against which the appropriateness of individual norms can be assessed. In order to structure the observations, the platform phenomenon will first be contrasted with that of the EU (I.), before the characteristics of platform law as supranational law (II.) and European administrative law (III.), as well as questions of the platform-related protection of fundamental rights are addressed (IV.).

Mobile Health Interventions: A Frontier for Mitigating the Global Burden of Cardiovascular Disease.

Mobile health (mHealth) interventions have emerged as a promising approach for cardiovascular disease (CVD) prevention and management. The proliferation of smartphones and wearable devices enables convenient access to health monitoring tools, educational resources, and communication with healthcare providers. mHealth interventions encompass mobile apps, wearables, and telehealth services that empower users to monitor vital signs, adhere to medication, and adopt healthier lifestyles. Their effectiveness hinges on user engagement, leveraging behavioral science principles and gamification strategies. While mHealth offers advantages such as personalized support and increased reach, it faces challenges pertaining to data privacy, security concerns, and resistance from healthcare providers. Robust encryption and adherence to regulations like the Health Insurance Portability and Accountability Act (HIPAA) are crucial for safeguarding sensitive health data. Integrating mHealth into clinical workflows can enhance healthcare delivery, but organizational adjustments are necessary. The future of mHealth is closely intertwined with artificial intelligence (AI), enabling remote monitoring, predictive algorithms, and data-driven insights. Tech giants are incorporating advanced health-tracking capabilities into their devices, paving the way for personalized wellness approaches. However, mHealth grapples with ethical dilemmas surrounding data ownership, privacy breaches, and inadvertent data capture. Despite its potential, mHealth necessitates a concerted effort to overcome obstacles and ensure ethical, secure, and practical implementation. Addressing technical challenges, fostering standardization, and promoting equitable access are pivotal for unlocking the transformative impact of mHealth on cardiovascular health and reducing the global burden of CVD.

Non-fungible tokens (NFTs) in healthcare: a thematic analysis and research agenda.

In the big data era, where corporations commodify health data, non-fungible tokens (NFTs) present a transformative avenue for patient empowerment and control. NFTs are unique digital assets on the blockchain, representing ownership of digital objects, including health data. By minting their data as NFTs, patients can track access, monetize its use, and build secure, private health information systems. However, research on NFTs in healthcare is in its infancy, warranting a comprehensive review. This study conducted a systematic literature review and thematic analysis of NFTs in healthcare to identify use cases, design models, and key challenges. Five multidisciplinary research databases (Scopus, Web of Science, Google Scholar, IEEE Explore, Elsevier Science Direct) were searched. The approach involved four stages: paper collection, inclusion/exclusion criteria application, screening, full-text reading, and quality assessment. A classification and coding framework was employed. Thematic analysis followed six steps: data familiarization, initial code generation, theme searching, theme review, theme definition/naming, and report production. Analysis of 19 selected papers revealed three primary use cases: patient-centric data management, supply chain management for data provenance, and digital twin development. Notably, most solutions were prototypes or frameworks without real-world implementations. Four overarching themes emerged: data governance (ownership, tracking, privacy), data monetization (commercialization, incentivization, sharing), data protection, and data storage. The focus lies on user-controlled, private, and secure health data solutions. Additionally, data commodification is explored, with mechanisms proposed to incentivize data maintenance and sharing. NFTs are also suggested for tracking medical products in supply chains, ensuring data integrity and provenance. Ethereum and similar platforms dominate NFT minting, while compact NFT storage options are being explored for faster data access. NFTs offer significant potential for secure, traceable, decentralized healthcare data exchange systems. However, challenges exist, including dependence on blockchain, interoperability issues, and associated costs. The review identified research gaps, such as developing dual ownership models and data pricing strategies. Building an open standard for interoperability and adoption is crucial. The scalability, security, and privacy of NFT-backed healthcare applications require further investigation. Thus, this study proposes a research agenda for adopting NFTs in healthcare, focusing on governance, storage models, and perceptions.

What do secondary school pupils eat at school? The barriers experienced in collecting transactional data from canteen purchases.

Introduction & BackgroundWelsh secondary schools generally use a cashless catering system and pupils pay for school food and drink via contactless cards, thumb or fingerprint biometric scanning. Helpfully, the digital footprint of school canteen purchase data already exists and is continually compiled over a limitless period. Compared to other methods of recording dietary intake (i.e., questionnaires and food diaries), utilising this transactional data is an unobtrusive method of data collection and has great potential. Although individual-level transactional data is being amassed, it remains unexploited by either the school, local authorities or Welsh Government. Obtaining this anonymised individual-level transactional data would provide immense insight into what pupils purchase throughout the school day. Objectives & ApproachThe Welsh School Meals (WSM) project aimed to investigate the feasibility of using secondary school canteen transactional data to better understand what pupils purchase during the school day and its nutritional quality. Semi-structured interviews have been conducted with a representative from all cashless system providers (n=7) used in Welsh secondary schools. Next, the WSM project initially aimed to recruit nine secondary schools, with the methodological plan to: (i) liaise with head teachers; (ii) mine data; (iii) interview catering managers and head teachers; (iv) facilitate nutritional analysis; (v) conduct focus groups with pupils; and (vi) co-produce case studies. Relevance to Digital FootprintsSchool canteen transaction data is a form of digital footprint and utilising it to understand the current landscape of food and drink choices during the school day will inform Public Health policymakers and practice. ResultsDespite trying numerous strategies, the WSM project has encountered blockages which have prevented data acquisition. The four key stumbling blocks faced were: (i) identifying data providers; (ii) identifying data owners; (iii) data sharing; and, (iv) engaging stakeholders. Only the first barrier has been overcome and despite starting school recruitment in May 2023, the latter three barriers have stalled any progress. Conclusions & ImplicationsExploiting existing cashless catering system technology to collect individual-level big data from school canteen transactions has enormous potential. However, the WSM project has concluded that obtaining this data was not feasible.

Protecting Data at Risk of Unintentional Electromagnetic Emanation: TEMPEST Profiling

Unintentional electromagnetic (EM) emissions often include information about the data processed by electronic devices. Intrusion based on an unintentional EM emission leaves no evidence of an attacker’s activity, while the data owner is unaware that it has been lost. EM attacks can be performed without physically damaging a device that operates regularly. The most typical intrusion activities involve sensitive data exfiltration using various methods that do not require the physical connection of devices to the computer network or communication channels. This research examines EM emissions from computer monitors, wireless keyboards and mice, printers, scanners, conductors, piezoelectric sensors (PES), and radio frequency identification (RFID) devices. The telecommunication electronics material protected from emanating spurious transmissions (TEMPEST) profiling as a performance engineering of the EM footprint is discussed. This study also presents different TEMPEST standards and highlights their importance concerning unintentional EM radiation.

EVALUATION OF DDOS INVASIONS

Cloud storage providers have widely integrated data deduplication techniques to manage the exponential growth of data. Various secure strategies for data deduplication have been developed to bolster the security of client data within shared storage environments. One critical area of focus is ensuring Re- encrypting deduplicated encrypted data in a safe and effective manner, which has garnered significant attention from researchers. However, recent investigations have uncovered vulnerabilities in the lightweight encrypted deduplication technique with rekeying awareness (REED), particularly susceptible to an assault via a stub-reserved. In response, We provide a reliable data deduplication method that uses effective re-encryption. leveraging Combined Win Big or Bust Transformation (CAONT) and segments that were chosen at random from the Sprout channel. Our approach is resilient protects sensitive data privacy and is resistant to the stub- reserved assault through one-way hash functions. Furthermore, our method minimizes computation overheads by requiring data owners to re-encrypt only a fraction of the package using CAONT, rather than the entire package. Through rigorous security analysis andexperiments, we validate the efficacy and safety of our re- encryption strategy. KEY WORDS: - Cloud storage, data deduplication, security, sensitive information, outsourced storage.

Enabling Parity Authenticator-Based Public Auditing With Protection of a Valid User Revocation in Cloud

The significance of the cloud enables the data owners (DOs) to store data remotely in cloud server (CS). The external and internal attacks on the stored data at CS can deliberately remove data. Furthermore, the CS removes the stored data to make empty location for the user's upcoming new data. However, it is a legal expectation of DOs to know whether their data are correctly stored or altered in CS. In this article, we propose a novel privacy-aware and hash-parity-bits-based public auditing (PA-HPPA) framework to secure full data, left half of the data, and the right half of the data, generated by a DO. DO generates two private key pairs with the assistance of a virtual key and a user ID (IP). The virtual key is the sequence number of DO who is registered and provided by the trusted data manager (TDM) while IP is the sequence number of DO working in an organization. Subsequently, DO blinds the categorized data and generates their signatures and hashes. In addition, DO generates the parity bits using xor and assigns to each hard drive (HD) in CS, which assistants to TDM in public auditing. Second, how to identify the error in the stored data and how to securely recover the error/missed data? Extension to the framework, the novel proposed data error identification and secure data recovery produce tags for installed HDs of CS using truth table and recover the altered/missed data via a authenticator, which is produced using xor function. Third, how to protect a valid user from revocation and, in case a user has revoked on merit basis, then how to securely access the stored data of it? This novel work has proposed three conditions to meet the validity of the valid user from revocation and securely generating the public-private key pairs to access the stored data of the revoked user securely from CS. Fourth, there is an efficient novel proposed dynamic operation scheme to insert, update, or delete the stored data at CS without regenerating the signatures, hashes, and tags for the whole stored data in cloud. The security analysis and the performance evaluation of the proposed solutions are provably efficient and secure with reduced communication costs.

Split Learning with Differential Privacy for Integrated Terrestrial and Non-Terrestrial Networks

Integrated terrestrial and non-terrestrial networks (TNTNs) have become promising architecture for enabling ubiquitous connectivity. Smart remote sensing is one of the typical applications of TNTNs that collects and analyzes various dimensions of remote sensing data by deploying Internet of Things (IoT) sensors and edge computing in terrestrial, space, aerial, and underwater networks. To improve the analysis accuracy of remote sensing data, the owners of different networks should conduct collaborative learning on different dimensions of data, while data and label privacy should be jointly considered. However, the existing collaborative learning paradigms are difficult to meet this demand. In this article, we propose a paradigm of split learning (SL), where the data owner and the label owner train different parts of the deep learning model and only exchange the intermediate data (i.e., smashed data and cut layer gradients). We explore the potential privacy attacks that recover the raw data and label information based on the intermediate data and propose the differential privacy (DP) based defense mechanisms that inject randomly generated Laplace noise into the intermediate data to disturb the attack performance. We also conduct a simulation study based on a real-world satellite remote sensing dataset to validate that the SL paradigm with defense mechanisms can effectively balance the performance of the collaborative model training and the protection of the data and label privacy. Finally, we discuss the main challenges and potential research directions of the privacy-preserving SL paradigm for smart remote sensing over TNTNs.

Data ownership in finance

Data ownership in finance

The (im)possibility of personal and industrial (machine-generated) data to be subject to property rights

Abstract Discussions surrounding the possibility of expanding the legal regime of ownership to data have been ongoing for several decades. Proponents of recognizing the right of ownership to data motivate their position by the needs of the data economy and the need to protect the interests of data subjects. However, specific properties of data cause contradictions with the concept of property rights. This paper examines data in a system of related categories, such as information and knowledge, aiming to determine their correlation and answer the question of whether personal and industrial (machine-generated) data can be objects of ownership. The category of digital data is analysed; its specific features, which make it possible to equate them with a type of property, are determined. The paper reveals correlation between concepts of digital ownership and data ownership and answers the question of why there is no room for personal and industrial data in the ownership paradigm.

How to build scalability into the design and implementation of an enterprise data framework

This paper begins by discussing the need to actively manage data as a valuable enterprise asset. It explores what an enterprise data framework (EDF) looks like, before identifying the elements that are key to creating scalability. These are: 1) speaking the language of the executive and gaining longterm investment and continued support; and 2) designing-in scalability through understanding the landscape, hooking into existing practices, ensuring the right data ownership, roles and responsibilities, avoiding the fool’s path by designing-in flexibility, and having strong triage processes. The paper then acknowledges that scalability is only possible through successful adoption of the framework. The paper finishes by examining who leads the implementation and what a successful chief data officer (CDO) looks like, how the creation of a movement is essential and how key culture is. It concludes that implementing an EDF into a live commercial environment is a matter of iterations and that moving through the maturity curve requires adaptability and resilience by not just the framework but also the CDO and the data function. Three guiding principles are identified: 1) relevance; 2) impact; and 3) being a good ancestor.

Federated Learning and Unlearning as Enablers of Wind Turbine Digital Twins

Real-time capable models are paramount for the successful adaptation of digital twin technology into industries such as wind energy, but high-fidelity physics-based models cannot achieve the required speed, while data-driven and hybrid methods require large amounts of training data which is typically confidential. In this work, the combination of federated learning with hybrid modeling is proposed to train fast and reliable models across multiple confidential data sets owned by different stakeholders. The approach is demonstrated on physics-guided neural networks to estimate the lift and drag of wind turbine airfoils. A scenario is devised where multiple confidential data subsets are confined to different client devices. It is shown that the physics-guided neural networks can be trained through federated learning across those devices and data subsets and that the resulting models can recover and even surpass the accuracy of a model that is trained conventionally by merging the data subsets on a single device. The presented approach is highly scalable and can be easily adapted to many other applications. This work also discusses federated unlearning methods, which allow data owners to remove all traces of a data subset used in training if they decide to revoke their contribution.

Blockchain-assisted full-session key agreement for secure data sharing in cloud computing

Data sharing in cloud computing allows multiple data owners to freely share their data resources while security and privacy issues remain inevitable challenges. As a foundation of secure communication, authenticated key agreement (AKA) scheme has been recognized as a promising approach to solve such problems. However, most existing AKA schemes are based on the cloud-based architecture, privacy and security issues will inevitably occur once the centralized authority is attacked. Besides, most previous schemes require an online registration authority for authentication, which may consume significant resources. To address these drawbacks, for secure data sharing in cloud computing, a blockchain-assisted full-session key agreement scheme is proposed. After the registration phase, the registration authority does not engage in authentication and key agreement process. By utilizing blockchain technology, a common session key between the remote user and cloud server can be negotiated, and a shared group key among multiple remote users can be negotiated without private information leakage. Formal and informal security proof demonstrated the proposed scheme is able to meet the security and privacy requirements. The detail performance evaluation shows that the proposed scheme has lower computation costs and acceptable communication overheads while superior security is ensured.

A Prototype of Decentralized Applications (DApps) Population Management System Based on Blockchain and Smart Contract

The Indonesian population reached 270,20 million in 2020. Each resident is equipped with various secret identities. The COVID-19 pandemic has made all activities use technology as a basis, causing residents' identities to be stored digitally. Some applications that keep these identities experience data leaks. However, with the advent of Web3 and its emphasis on decentralization through blockchain, a new era of secure data management is possible. Blockchain, with its inherent security features, ensures that data stored is secure, difficult to damage or lose due to mutual consensus. Every transaction is recorded, making it easy to carry out the audit process. Therefore, this research will design and implement prototype dApps for secure population management, leveraging the superior security of blockchain technology. The initial stage of research is to conduct a literature study. Furthermore, it is to create designs such as system, infrastructure, and activity diagrams. Then do the development of the dApps prototype. The last is testing using OWASP ZAP and cost analysis. A dApps prototype was implemented on a blockchain. Every transaction is recorded and publicly viewable through the Etherscan platform. Other data stored on a blockchain have gone through an AES-256 encryption process with the data owner's account key so that the owner can only see the data. The results of the tests performed show that there is no high-level warning. The cost analysis results show that the most used costs are when deploying smart contracts and making new data. For further development, it is implementing permissionless blockchain and multi-accounts.

A Decentralized Private Data Marketplace using Blockchain and Secure Multi-Party Computation

Big data has proven to be a very useful tool for companies and users, but companies with larger datasets have ended being more competitive than the others thanks to machine learning or artificial intelligence. Secure multi-party computation (SMPC) allows the smaller companies to jointly train arbitrary models on their private data while assuring privacy, and thus gives data owners the ability to perform what are currently known as federated learning algorithms. Besides, with a blockchain it is possible to coordinate and audit those computations in a decentralized way. In this document, we consider a private data marketplace as a space where researchers and data owners meet to agree the use of private data for statistics or more complex model trainings. This document presents a candidate architecure for a private data marketplace by combining SMPC and a public, general-purpose blockchain. Such a marketplace is proposed as a smart contract deployed in the blockchain, while the privacy preserving computation is held by SMPC.

Personhood Global Whispers: Ethical Echoes of Decentralization?

This article explores the ethical dilemmas propelled by a significant shift in the allocation of trust and intelligence due to blockchain technology and AI, resulting in a notable decrease in transaction costs. The ethical and political implications of democratizing the resulting productivity gains are noteworthy, and while the pie is expanding, how its slices are distributed remains an open question. Enter Worldcoin, an innovative worldwide initiative that creates an identity system based on proof of personhood and zero-knowledge proofs (ZKP) to provide everyone with a distinct and anonymous "World ID. Using the author's “cyberethics-mix" framework, this paper examines the possible implications of such a system concerning data's protection, ownership, accuracy, and accessibility, underscoring the ethical significance of a political approach emphasizing inclusivity and sustainability through digital decentralization.