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This article reconceptitalises the term of "Fintech" by examining five key technological innovations in international commerce and finance: Al, blockchain, cloud computing, data analytics, and e-commerce. U then considers relevant legal and regulatory issuesforfintech companies, including the regulatory toolkits, data protection legislation, and corporate finance law. [ABSTRACT FROM AUTHOR]

The proposal for a regulation on the European Health Data Space (EHDS) is a much-awaited project. It aspires to create a harmonised framework – a common European data space – for the administration of health data (primary use) across Member States and the promotion of healthcare research and innovation (by establishing rules for the secondary use of health data). As such, although the EHDS proposal is a legal document, in its essence, it includes provisions that introduce not only legal, but also institutional, and technical-infrastructural changes. Overall, together with the Regulation 2017/745 on medical devices, the Data Governance Act (DGA), the Data Act, the AI Act, and the General Data Protection Regulation (GDPR), the EHDS proposal will complete the regulatory canvas for the use of health data in the European Union. Although we are supportive of the EHDS initiative, there are aspects of the proposal that require further debate, reconsideration, and amendments. Following previous work on potential power asymmetries encapsulated in the Proposal, in this commentary, we focus on the provisions of/for interoperability of the Electronic Health Record (EHR) systems (Ar. 14–32) as well as the provisions on the structure of Health Data Access bodies and their cross-border organisation (section 3). We recommend a series of amendments to orientate the EHDS project better to its constitutive goals: the promotion of public health research and respect for the rights of the individuals. [ABSTRACT FROM AUTHOR]

With the Internet's rapid expansion, network security challenges have become increasingly complex and prominent. Traditional protection methods, largely dependent on predefined rules and patterns, demonstrate limited effectiveness against sophisticated and unknown network attacks, failing to harness the full potential of extensive network data. This study addresses the challenges faced by modern cybersecurity, particularly the limitations of traditional defense methods in countering unknown and complex attacks, by proposing a solution that integrates data analysis and machine learning technologies. The focus of this research is placed on network security anomaly detection as well as on intelligent network operations and maintenance exception management based on graph network algorithms, aiming to enhance security defense capabilities and operational efficiency. Specifically, the main contributions and innovations of this paper include: 1. Innovations in sampling, aggregation, and loss functions within the Graph Sample and Aggregation (GraphSAGE) model to improve the accuracy and robustness of the model for network anomaly detection; 2. The introduction of a novel network anomaly root cause analysis and localization model, which, combined with an optimized root cause likelihood assessment method and search scheme, significantly enhances the speed and accuracy of anomaly localization; 3. The design of an integrated decision support system that can automatically adjust protection strategies as network conditions change, achieving a high level of automation and intelligence in cybersecurity management. This work not only provides effective technical support for network security protection but also opens new avenues for future cybersecurity research. [ABSTRACT FROM AUTHOR]

Patient privacy and data protection have been crucial concerns in Ehealthcare systems for many years. In modern-day applications, patient data usually holds clinical imagery, records, and other medical details. Lately, the Internet of Medical Things (IoMT), equipped with cloud computing, has come out to be a beneficial paradigm in the healthcare field. However, the openness of networks and systems leads to security threats and illegal access. Therefore, reliable, fast, and robust security methods need to be developed to ensure the safe exchange of healthcare data generated from various image sensing and other IoMT-driven devices in the IoMT network. This paper presents an image protection scheme for healthcare applications to protect patients' medical image data exchanged in IoMT networks. The proposed security scheme depends on an enhanced 2D discrete chaotic map and allows dynamic substitution based on an optimized highly-nonlinear S-box and diffusion to gain an excellent security performance. The optimized S-box has an excellent nonlinearity score of 112. The new image protection scheme is efficient enough to exhibit correlation values less than 0.0022, entropy values higher than 7.999, and NPCR values around 99.6%. To reveal the efficacy of the scheme, several comparison studies are presented. These comparison studies reveal that the novel protection scheme is robust, efficient, and capable of securing healthcare imagery in IoMT systems. [ABSTRACT FROM AUTHOR]

In an age where technological personalisation beats privacy, individuals willingly give their data in return for a bespoke experience. Individuals are more likely to share their data when they know there are clear benefits to themselves or society. The opportunity cost in handing over personal data in return for a personalised experience can open the door to privacy breaches and criminal activity. Forming part of personal data is biometric data, which includes facial features, iris and retina, voice, and heartbeat. However, in Australia, a uniform legislated definition of biometric data does not exist. Every day, open sources grow with biometric data. A better understanding of the ways in which biometric data can be used and a best practice definition is key. This essay seeks to highlight, analyse, and ultimately resolve the legal and policy risks of not having a legislated definition of biometric data for privacy purposes. [ABSTRACT FROM AUTHOR]

21stcentury businesses relyon leveraging consumerdata totargetconsumers more accurately, customise services and learn more about the markets in which they operate. As the use of data in business has flourished, incidents of data breaches have also increased with criminal activity, and system and human errors all playing a role in the unintended or unauthorised release of consumer data. Recent increases in the prominence of data breaches have raised questions about the obligations to protect the consumer data at law. This article undertakes a practical and example-driven review of the Australian laws applicable to data protection - primarily, the Privacy Act 1988 (Cth). This article will also briefly touch on the provisions of the Consumer Data Right as an example of a comparative approach of mandating data protection. As Australian law currently stands, corporate data protection obligations are flexible and circumstance dependent, but do not impose prescriptive obligations. [ABSTRACT FROM AUTHOR]

Industry 5.0 represents a revolutionary paradigm in industrial engineering that extends and develops the concepts previously introduced by Industry 4.0, a significant leap in industrial engineering with the potential to bring significant benefits in terms of efficiency, sustainability and innovation. However, this transformation must be carefully managed to ensure that all aspects of security, data protection and social impact are adequately addressed. This approach brings a number of significant innovations that have the potential to radically transform the manufacturing environment. and industrial processes. [ABSTRACT FROM AUTHOR]

Covid-19 coronavirus pandemic has affected higher education institutions all over the world, causing face-to-face education to cease. Schools have tried to carry out educational activities through online teaching either by using on premise infrastructure or by leasing cloud based online platforms. Although these platforms are convenient, most of them do not meet all the requirements for higher education institutions. Not knowing where the personal data is stored in a public cloud creates another problem for some countries according to laws. In this study, a new online learning platform has been developed for higher education institutions to solve these problems using state-of-the-art cloud technologies. The new system enables implementation of individual curricula of many higher education institutions in one software system, and it can be taken into service quickly in emergencies. It expands dynamically by activating a large number of streaming servers to meet the demand. The new system provides easy to use-learn interfaces, offers an economical solution for e-learning by sharing the resources, and compliant with the law on protection of personal data. The new platform was in service at 12 universities in Turkiye during the fall 2020, and its performance was measured with surveys at various levels. [ABSTRACT FROM AUTHOR]

Cybercrime, particularly Sniffing, poses a hazard in the digital age since attackers illegally breach customers' data, resulting in losses and widespread concern. This study aimed to clarify the legal protections available to victims of sniffing cybercrimes and the banks' obligations to their clientele. This study includes primary, secondary, and tertiary legal materials with a normative legal approach. The analytical method produced information through descriptive analysis using qualitative methodologies. The research findings demonstrated that, despite the lack of a required special institution and ambiguity surrounding accountability in resolving disputes deemed harmful to customers, Law No. 27 of 2022 and Bank Indonesia Regulation No. 3 of 2023 indicated that there was legal protection for customers' data. As a result, customers must take greater precautions to protect their data.

IntroductionDespite the well-known benefits of exercise-based cardiac rehabilitation for the secondary prevention of cardiovascular disease, participation in cardiac rehabilitation programmes and adherence to secondary prevention recommendations remain limited. Digital technologies have the potential to address low participation and adherence but attempts at implementing digital health interventions in real-life clinical practice frequently encounter various barriers. Studies about patients' experiences and perspectives regarding the use of digital technology can assist developers, researchers and clinicians in addressing or pre-empting patient-related barriers. This study was therefore conducted to investigate the experiences and perspectives of cardiac rehabilitation patients in Austria with regard to using digital technology for physical activity and exercise.MethodsTwenty-five current and former cardiac rehabilitation patients (18 men and 7 women, age range 39 to 83) with various cardiac conditions were recruited from a clinical site in Salzburg, Austria. Semi-structured qualitative interviews were audio-recorded and transcribed verbatim. The analysis followed a descriptive phenomenological approach, applying the framework analysis method.ResultsThe sample was diverse, including interviewees who readily used digital devices to support their physical activity, exercise and health monitoring, and interviewees who did not. Simplicity, convenience and accessibility were highlighted as important facilitators for the use of digital technology, while annoyance with digital devices, concerns about becoming dependent on them, or simply a preference to not use digital technology were commonly stated reasons for non-use. Interviewees' views on data protection, data sharing and artificial intelligence revealed wide variations in individuals' prior knowledge and experience about these topics, and a need for greater accessibility and transparency of data protection regulation and data sharing arrangements.DiscussionThese findings support the importance that is attributed to user-centred design methodologies in the conceptualisation and design of digital health interventions, and the imperative to develop solutions that are simple, accessible and that can be personalised according to the preferences and capabilities of the individual patient. Regarding data protection, data sharing and artificial intelligence, the findings indicate opportunity for information and education, as well as the need to offer patients transparency and accountability in order to build trust in digital technology and digital health interventions.

Connected cars are becoming commonplace, creating vast volumes of data that may contain or reveal information about drivers. It is imperative to understand drivers' perspectives on such data being collected and used by car manufacturers. Applying the Human-Data Interaction (HDI) framework - which centres the user and their experience - to this context, we conducted semi-structured interviews with 15 drivers. Interview transcripts revealed issues with understanding of car data (Legibility) and drivers’ sense of control over automotive data (Agency), across different circumstances (Negotiability). Our findings suggest that car manufacturers should enable learning, access, and control over car data via the mobile app in a coordinated fashion, as the privacy preferences of drivers are often based on perceived benefit or threat resulting from data collection. The ability to set data-sharing preferences in a time- and location- sensitive manner can help drivers navigate data sharing consent based on circumstances. These findings have implications for the consent procedures in modern cars as well as for the development of data-sharing programmes aimed at creation of climate-smart cities.

This research paper proposes an intrusion detection system that combines particle swarm optimization and AdaBoost algorithms to classify and detect malware-related records in innovative health app platforms. The research employs the NSL KDD dataset, consisting of 125,973 instances and 41 features, with data split into training (20 %) and testing (80 %) sets. A feature selection process using Particle Swarm Optimization identifies 12 relevant features for intrusion detection. The intrusion detection system effectively categorizes various attack types, including Denial of Service (DoS), User-to-root (U2R), Root-to-local (R2L), and Probe attacks. Regarding classifier performance, AdaBoost exhibits the highest recall value (0.966667), highlighting its strong intrusion detection capabilities. The experimental results demonstrate that the PSO-AdaBoost approach achieves superior accuracy, precision, and recall in intrusion detection. By integrating machine learning-based intrusion detection systems into smart health apps, healthcare providers can enhance patient care, reduce costs, and ensure the confidentiality of sensitive medical information. This research highlights the potential of ML-IDSs in strengthening the security infrastructure surrounding medical IoT devices and improving patient outcomes in the interconnected world of the Internet of Medical Things.

Large European research consortia in the health sciences face challenges regarding the governance of personal data collected, generated and/or shared during their collective research. A controller in the sense of the GDPR is the entity which decides about purposes and means of the data processing. Case law of the Court of Justice of the European Union (CJEU) and Guidelines of the European Data Protection Board (EDPB) indicate that all partners in the consortium would be joint controllers. This paper summarises the case law, the Guidelines and literature on joint controllership, gives a brief account of a webinar organised on the issue by Lygature and the MLC Foundation. Participants at the webinar agreed in large majority that it would be extreme if all partners in the consortium would become joint controllers. There was less agreement how to disentangle partners who are controllers of a study from those who are not. In order to disentangle responsibilities, we propose a funnel model with consecutive steps acting as sieves in the funnel. It differentiates between two types of partners: all partners who are involved in shaping the project as a whole versus those specific partners who are more closely involved in a sub-study following from the DoA i or the use of the data Platform. If the role of the partner would be comparable to that of an outside advisor, that partner would not be a data controller even though the partner is part of the consortium. We propose further nuances for the disentanglement which takes place in various steps. Uncertainty about formal controllership under the GDPR can stifle collaboration in consortia due to concerns over (shared) responsibility and liability. Data subjects’ ability to exercise their right can also be affected by this. The funnel model proposes a way out of this conundrum.

Recording and transcribing interviews in qualitative social research is a vital but time-consuming and resource-intensive task. To tackle this challenge, researchers have explored various alternative approaches; automatic transcription utilising speech recognition algorithms has emerged as a promising solution. The question of whether automated transcripts can match the quality of transcripts produced by humans remains unanswered. In this paper we systematically compare multiple automatic transcription tools: Amberscript, Dragon, F4x, Happy Scribe, NVivo, Sonix, Trint, Otter, and Whisper. We evaluate aspects of data protection, accuracy, time efficiency, and costs for an English and a German interview. Based on the analysis, we conclude that Whisper performs best overall and that similar local-automatic transcription tools are likely to become more relevant. For any type of transcription, we recommend reviewing the text to ensure accuracy. We hope to shed light on the effectiveness of automatic transcription services and provide a comparative frame for others interested in automatic transcription.

This paper will discuss the European funded iToBoS project, tasked by the European Commission to develop an AI diagnostic platform for the early detection of skin melanoma. The paper will outline the project, provide an overview of the data being processed, describe the impact assessment processes, and explain the AI privacy risk mitigation methods being deployed. Following this, the paper will offer a brief discussion of some of the more complex aspects: (1) the relatively low population clinical trial study cohort, which poses risks associated with data distinguishability and the masking ability of the applied anonymisation tools, (2) the project's ability to obtain informed consent from the study cohort given the complexity of the technologies, (3) the project's commitment to an open research data strategy and the additional privacy risk mitigations required to protect the multi-modal study data, and (4) the ability of the project to adequately explain the outputs of the algorithmic components to a broad range of stakeholders. The paper will discuss how the complexities have caused tension which are reflective of wider tensions in the health domain. A project level solution includes collaboration with a melanoma patient network, as an avenue for fair and representative qualification of risks and benefits with the patient stakeholder group. However, it is unclear how scalable this process is given the relentless pursuit of innovation within the health domain, accentuated by the continued proliferation of artificial intelligence, open data strategies, and the integration of multi-modal data sets inclusive of genomics.